diff --git a/.Rbuildignore b/.Rbuildignore
index e0fa3f49ff3b0e20f022e14e7650a47f80433a41..67c99d4337a2fde8d4e0b1b53b9172bd117ad3ed 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -1,3 +1,5 @@
+^Meta$
+^doc$
^.*\.Rproj$
^\.Rproj\.user$
^.travis.yml$
@@ -6,4 +8,4 @@
^LICENSE.md$
^code.json$
^DISCLAIMER.md$
-^revisions.txt$
\ No newline at end of file
+^revisions.txt$
diff --git a/.gitignore b/.gitignore
index 076ebf66dc0ab8175cde3c4fbf975d111b1fb914..8ce5560a6c60e1122aac7115e4d25e0c908785e4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,40 +1,32 @@
+Meta
+doc
# History files
.Rhistory
.Rapp.history
# Session Data files
.RData
-
# Example code in package build process
*-Ex.R
-
# Output files from R CMD build
/*.tar.gz
-
# Output files from R CMD check
/*.Rcheck/
-
# RStudio files
.Rproj.user/
-
# produced vignettes
vignettes/*.html
vignettes/*.pdf
-
# OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3
.httr-oauth
-
# knitr and R markdown default cache directories
/*_cache/
/cache/
-
# Temporary files created by R markdown
*.utf8.md
*.knit.md
-
# Shiny token, see https://shiny.rstudio.com/articles/shinyapps.html
rsconnect/
-
# old, offending files:
**/User_Guide
**/previous_builds
diff --git a/DESCRIPTION b/DESCRIPTION
index 455980ca6747738027010059db27f0f639123e62..4a80c7f289a2577accea9503a4d2f35f4a5d6e90 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
Package: GenEst
Title: Generalized Mortality Estimator
-Version: 1.2.0
-Date: 2018-12-06
+Version: 1.2.1
+Date: 2018-12-18
Authors@R: c(
person("Daniel", "Dalthorp", , "ddalthorp@usgs.gov", c("aut", "cre")),
person("Juniper", "Simonis", , "simonis@dapperstats.com", "aut"),
@@ -31,9 +31,8 @@ Imports:
shiny,
shinyjs,
sticky,
- survival,
- zip
-RoxygenNote: 6.1.0
+ survival
+RoxygenNote: 6.1.1
Suggests:
knitr,
rmarkdown,
@@ -41,4 +40,3 @@ Suggests:
VignetteBuilder:
knitr
LinkingTo: Rcpp
-
diff --git a/DISCLAIMER.md b/DISCLAIMER.md
index 7ad2ce0e8cb19e2465f15556b54f9105c726b192..1584d24c63e5afe6dd4a74daeda26c451a40e9f8 100644
--- a/DISCLAIMER.md
+++ b/DISCLAIMER.md
@@ -1,4 +1,8 @@
-This software is preliminary or provisional and is subject to revision. It is
+This is an incremental update to GenEst v1.0.0 which was approved for release by the
+U.S. Geological Survey (USGS) after rigorous review. This software release is in
+compliance with the US Geological Survey publishing, which is associated with IP-101457.
+
+This update is preliminary or provisional and is subject to revision. It is
being provided to meet the need for timely best science. The software has not
received final approval by the U.S. Geological Survey (USGS). No warranty,
expressed or implied, is made by the USGS or the U.S. Government as to the
diff --git a/R/GenEst.R b/R/GenEst.R
index 1acc6540e0c5e623fb0eb695bbe3562590dcd475..0fa419bc36d3043c45a44cf16d25a9d365571a02 100644
--- a/R/GenEst.R
+++ b/R/GenEst.R
@@ -20,8 +20,7 @@
#' formula median model.matrix na.omit optim pgamma pnorm qnorm quantile
#' reformulate rnorm runif terms update.formula weighted.mean
#' @importFrom survival strata
-#' @importFrom utils combn packageDescription read.csv read.csv2 write.csv
-#' zip
+#' @importFrom utils combn packageDescription read.csv read.csv2 write.csv zip
#'
#' @title Generalized estimation of mortality
diff --git a/R/app_download_functions.R b/R/app_download_functions.R
index ff653c54752e48a4289a6d7d8ce311d76a47c335..92c8eeec1a5bbd412081084a6da498db5da4fea4 100644
--- a/R/app_download_functions.R
+++ b/R/app_download_functions.R
@@ -77,38 +77,24 @@ downloadgFig <- function(rv, sc){
#'
#' @export
#'
-downloadMFig <- function(rv, split = TRUE, transpose = FALSE){
-
+downloadMFig <- function(rv, split = TRUE){#, transpose = FALSE){
if (split){
- if (transpose){
downloadHandler(filename = "M_fig.png",
content = function(file){
png(file, height = rv$figH_M, width = rv$figW_M, units = "px")
tryCatch(
- plot(transposeSplits(rv$Msplit)),
+ plot(rv$Msplit, CL = rv$CL),
error = function(x){plotNA()}
)
dev.off()
}
)
- } else {
- downloadHandler(filename = "M_fig.png",
- content = function(file){
- png(file, height = rv$figH_M, width = rv$figW_M, units = "px")
- tryCatch(
- plot(rv$Msplit),
- error = function(x){plotNA()}
- )
- dev.off()
- }
- )
- }
- } else{
+ } else {
downloadHandler(filename = "M_fig.png",
content = function(file){
png(file, height = rv$figH_M, width = rv$figW_M, units = "px")
tryCatch(
- plot(rv$M),
+ plot(rv$M, CL = rv$CL),
error = function(x){plotNA()}
)
dev.off()
@@ -146,7 +132,7 @@ downloadTable <- function(filename, tablename, csvformat){
#' @description Handle the downloading of a data set
#'
#' @param set the name of the data set to download
-#' @param csvformat Format of .csv files to download. For comma field
+#' @param csvformat Format of .csv files to download. For comma field
#' separator and period decimal separator, use \code{csvformat = NULL} or "".
#' For semicolon field separator and comma decimal separator, use
#' \code{csvformat = 2}.
@@ -155,12 +141,12 @@ downloadTable <- function(filename, tablename, csvformat){
#' @export
#'
downloadData <- function(set, csvformat = NULL){
- fpre <- switch(set, "mock" = "",
- "powerTower" = "solar_",
+ fpre <- switch(set, "mock" = "",
+ "powerTower" = "solar_",
"PV" = "solar_",
- "trough" = "solar_",
+ "trough" = "solar_",
"cleared" = "wind_",
- "RP" = "wind_",
+ "RP" = "wind_",
"RPbat" = "wind_")
filename <- paste0(fpre, set, ".zip")
exob <- get(paste0(fpre, set))
@@ -174,10 +160,8 @@ downloadData <- function(set, csvformat = NULL){
filename = filename,
content = function(file) {
tozip <- paste0(pth, "/", names(exob), "_", set, ".csv")
- #zip(zipfile = file, files = tozip, flags = c("-q", "-j"))
- zip::zip(zipfile = file, files = tozip)
+ utils::zip(zipfile = file, files = tozip, flags = c("-q", "-j"))
},
contentType = "application/zip"
)
}
-
diff --git a/R/app_functions.R b/R/app_functions.R
index c6b38af46b4f0da372f9e81ae4429f6b1688ef9f..45d99dddac049ca0f49e0d350613036c4368f231 100644
--- a/R/app_functions.R
+++ b/R/app_functions.R
@@ -7,10 +7,8 @@
#' @export
#'
runGenEst <- function(){
-
appDir <- system.file("app", package = "GenEst")
- runApp(appDir)
-
+ runApp(appDir, launch.browser = T)
}
diff --git a/R/app_msg_functions.R b/R/app_msg_functions.R
index 27319af7e9e76b2bc8c68edcfa2d4e189bd9384b..1dd3415923cc3ec3e8b04805b24ee913c5dcccf0 100644
--- a/R/app_msg_functions.R
+++ b/R/app_msg_functions.R
@@ -308,7 +308,7 @@ msgModFail <- function(mods, type = "SE", special = NULL){
} else if (special == "size_k"){
msg <- "Some size classes had no successful models. Consider a fixed k."
} else if (special == "NA_kFixed"){
- msg <- "Fixed k check box selected but no or invalid value entered."
+ msg <- "invalid value entered for fixed k"
}
}
if (type == "g"){
diff --git a/R/app_output_utilities.R b/R/app_output_utilities.R
index 0d8bc2b8feed29040f3a38a969bf73b06db1a5ed..dfb04d907298526735c5d62ae718dc5ce0b90e20 100644
--- a/R/app_output_utilities.R
+++ b/R/app_output_utilities.R
@@ -24,7 +24,8 @@ classText <- function(rv, type = "SE"){
}
if (type == "g"){
if (length(rv$sizeclasses_g) > 1){
- out <- paste0("Size class: ", rv$sizeclass_g)
+ out <- paste0("Size class: ", rv$sizeclass_g, " ........ Search schedule: I = ",
+ round(rv$SS[["I"]],1), ", span = ", rv$SS[["span"]])
}
}
renderText(out)
@@ -121,8 +122,6 @@ reNULL <- function(x, toNULL){
#' @export
#'
initialOutput <- function(rv, output){
- output$SStext <- renderText(rv$SStext)
-
output$download_RP <- downloadData("RP")
output$download_RPbat <- downloadData("RPbat")
output$download_cleared <- downloadData("cleared")
diff --git a/R/app_panels.R b/R/app_panels.R
index 48182bff218c8b61a1e2888f2905e074c9661af9..0b1636c280a1d82a4243ba9b454c225eee6261a0 100644
--- a/R/app_panels.R
+++ b/R/app_panels.R
@@ -52,30 +52,30 @@ selectedDataPanel <- function(modType){
tName <- switch(modType,
"SE" = "Selected Data",
- "CP" = "Selected Data",
- "g" = "Schedule")
+ "CP" = "Selected Data")#,
+# "g" = "Summary")
Condition1 <- switch(modType,
"SE" = "input.obsSE == null",
- "CP" = "input.ltp == null | input.fta == null",
- "g" = NULL)
+ "CP" = "input.ltp == null | input.fta == null")#,
+# "g" = NULL)
Condition2 <- switch(modType,
"SE" = "output.filename_SE != null &
input.obsSE != null",
"CP" = "output.filename_CP != null & input.ltp != null &
- input.fta != null",
- "g" = NULL)
+ input.fta != null")#,
+# "g" = NULL)
Text1 <- switch(modType,
"SE" = em("Select observation columns to view data"),
- "CP" = em("Select observation columns to view data"),
- "g" = b(u(big("Search Schedule:"))))
+ "CP" = em("Select observation columns to view data"))#,
+# "g" = list(br(), br(), textOutput("Run 'Estimate' to view results")))
Text2 <- switch(modType,
"SE" = em(textOutput("filename_SE")),
- "CP" = em(textOutput("filename_CP")),
- "g" = list(br(), br(), textOutput("SStext")))
+ "CP" = em(textOutput("filename_CP")))#,
+# "g" = NULL)
Data <- switch(modType,
"SE" = dataTableOutput("selected_SE"),
- "CP" = dataTableOutput("selected_CP"),
- "g" = NULL)
+ "CP" = dataTableOutput("selected_CP"))#,
+# "g" = NULL)
tabPanel(tName, br(),
conditionalPanel(condition = Condition1, Text1),
diff --git a/R/app_server.R b/R/app_server.R
index 0e5fb96f3cf78df0f9881f12d794fafe2ef56d7f..13186a05c02c9ce915bb9813bf9f4ca8661e4e1d 100644
--- a/R/app_server.R
+++ b/R/app_server.R
@@ -32,13 +32,11 @@
#' @export
#'
GenEstServer <- function(input, output, session){
-
rv <- initialReactiveValues()
output <- initialOutput(rv, output)
msgs <- msgList()
options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))
options(DT.options = list(pageLength = 25))
-
observeEvent(input$clear_all, eval(reaction("clear_all")))
observeEvent(input$file_SE, eval(reaction("file_SE")))
observeEvent(input$file_SE_clear, eval(reaction("file_SE_clear")))
@@ -77,8 +75,6 @@ GenEstServer <- function(input, output, session){
observeEvent(input$split_M_clear, eval(reaction("split_M_clear")))
observeEvent(input$transpose_split, eval(reaction("transpose_split")))
- observeEvent(input$useSSdata, eval(reaction("useSSdata")))
- observeEvent(input$useSSinputs, eval(reaction("useSSinputs")))
observeEvent(input$run_g, eval(reaction("run_g")))
observeEvent(input$run_g_clear, eval(reaction("run_g_clear")))
observeEvent(input$outgclass, eval(reaction("outgclass")))
@@ -109,7 +105,7 @@ GenEstServer <- function(input, output, session){
#' "outSEclass", "outSEp", "outSEk", "ltp", "fta", "predsCP", "run_CP",
#' "run_CP_clear", "outCPclass", "outCPdist", "outCPl", "outCPs",
#' "run_M", "run_M_clear", "split_M", "split_M_clear", "transpose_split",
-#' "useSSdata", "useSSinputs", "run_g", "run_g_clear", or "outgclass".
+#' "run_g", "run_g_clear", or "outgclass".
#'
#' @return \code{reaction}: An object of type
#' "\code{\link[base]{expression}}" returned from
@@ -128,8 +124,8 @@ reaction <- function(eventName){
"outSEclass", "outSEp", "outSEk", "ltp", "fta", "predsCP",
"run_CP", "run_CP_clear", "outCPclass", "outCPdist",
"outCPl", "outCPs", "run_M", "run_M_clear", "split_M",
- "split_M_clear", "transpose_split", "useSSdata",
- "useSSinputs", "run_g", "run_g_clear", "outgclass",
+ "split_M_clear", "transpose_split",
+ "run_g", "run_g_clear", "outgclass",
"load_RP", "load_RPbat", "load_cleared", "load_PV",
"load_trough", "load_powerTower", "load_mock")
@@ -210,12 +206,6 @@ reactionMessageDone <- function(eventName){
reactMsg <- 'msgs$ModM <<- msgModDone(msgs, rv, "split")'
}
- if (eventName == "useSSdata"){
- reactMsg <- 'msgs$SS <<- msgSSavgFail(msgs, rv)'
- }
- if (eventName == "useSSinputs"){
- reactMsg <- 'msgs$SS <<- msgSSinputFail(msgs, rv)'
- }
reactMsg
}
diff --git a/R/app_ui.R b/R/app_ui.R
index d734d81c13c021439e3629a61c1ba173a70bc72b..5124ec364da82e2541bf57cc8d7240e1ad970663 100644
--- a/R/app_ui.R
+++ b/R/app_ui.R
@@ -381,8 +381,6 @@ gSidebar <- function(){
br(), br(),
modelInputWidget("gSearchInterval"),
modelInputWidget("gSearchMax"),
- modelInputWidget("useSSinputs"),
- modelInputWidget("useSSdata"),
modelRunWidget("g"),
modelOutputWidget("g")
)
@@ -401,7 +399,7 @@ gSidebar <- function(){
gMainPanel <- function(){
mainPanel(
tabsetPanel(id = "analyses_g",
- selectedDataPanel("g"),
+# selectedDataPanel("g"),
modelOutputPanel("gFigures"),
modelOutputPanel("gSummary")
)
diff --git a/R/app_ui_utilities.R b/R/app_ui_utilities.R
index 99dd31b53d96a7e2d38b51fe562bbf4758cea2d1..c5a969f67406d7e965c49a928abf278a58010f14 100644
--- a/R/app_ui_utilities.R
+++ b/R/app_ui_utilities.R
@@ -145,10 +145,8 @@ GenEstInlineCSS <- function(...){
"#fta" = "margin-bottom: 15px",
"#predsCP" = "margin-bottom: 15px",
"#frac" = "margin-bottom: 15px",
- "#useSSinputs" = "margin-bottom: 20px",
"#gSearchInterval" = "margin-bottom: 15px",
"#gSearchMax" = "margin-bottom: 20px",
- "#useSSdata" = "margin-bottom: 15px",
"#run_SE" = "margin-bottom: 10px",
"#run_CP" = "margin-bottom: 10px",
"#run_M" = "margin-bottom: 10px",
diff --git a/R/app_update_input.R b/R/app_update_input.R
index 2f1bf3aedb8d5d2376ce8aac93940efbbfe6f399..d21427e4a88af1e90b85518bcbe18202455262ce 100644
--- a/R/app_update_input.R
+++ b/R/app_update_input.R
@@ -12,7 +12,7 @@
#' "outSEclass", "outSEp", "outSEk", "ltp", "fta", "predsCP", "run_CP",
#' "run_CP_clear", "outCPclass", "outCPdist", "outCPl", "outCPs",
#' "run_M", "run_M_clear", "split_M", "split_M_clear", "transpose_split",
-#' "useSSdata", "useSSinputs", "run_g", "run_g_clear", or "outgclass".
+#' "run_g", "run_g_clear", or "outgclass".
#'
#' @param rv Reactive values list for the GenEst GUI.
#'
@@ -31,8 +31,8 @@ update_input <- function(eventName, rv, input, session){
"outSEclass", "outSEp", "outSEk", "ltp", "fta", "predsCP",
"run_CP", "run_CP_clear", "outCPclass", "outCPdist",
"outCPl", "outCPs", "run_M", "run_M_clear", "split_M",
- "split_M_clear", "transpose_split", "useSSdata",
- "useSSinputs", "run_g", "run_g_clear", "outgclass",
+ "split_M_clear", "transpose_split",
+ "run_g", "run_g_clear", "outgclass",
"load_RP", "load_RPbat", "load_cleared", "load_PV",
"load_trough", "load_powerTower", "load_mock")
@@ -40,26 +40,27 @@ update_input <- function(eventName, rv, input, session){
stop("eventName missing or not in list of available eventNames")
}
- if(eventName == "clear_all"){
+ if(eventName == "clear_all" | grepl("load_", eventName)){
toReset <- c("file_SE", "predsSE", "obsSE", "outSEp", "outSEk",
"outSEclass", "DWPCol", "split_SS", "split_CO",
"modelChoices_SE1", "outgclass","file_CP", "predsCP", "ltp",
"fta", "outCPl", "outCPs", "outCPdist", "outCPclass",
- "modelChoices_CP1", "file_SS", "gSearchInterval",
- "gSearchMax", "file_DWP", "file_CO", "COdate")
+ "modelChoices_CP1", "file_SS", "file_DWP", "file_CO", "COdate",
+ "sizeCol")
lapply(toReset, reset)
-
- scc <- rv$colNames_size
- if (is.null(scc)){
- scc <- ""
- }
- scs <- rv$sizeCol
- if (is.null(scc)){
- scs <- ""
- }
+# scc <- rv$colNames_size
+# if (is.null(scc)){
+# scc <- ""
+# }
+# scs <- rv$sizeCol # why would the sizeCol not be cleared when all the data sets are?
+# if (is.null(scc)){
+# scs <- ""
+# }
updateSelectizeInput(session, "predsSE", choices = "")
updateSelectizeInput(session, "obsSE", choices = "")
- updateSelectizeInput(session, "class", choices = scc, selected = scs)
+# updateSelectizeInput(session, "class", choices = scc, selected = scs)
+# NOTE: the commented-out line glued the previous sizes onto the size menu
+ updateSelectizeInput(session, "class", choices = "")
updateSelectizeInput(session, "modelChoices_SE1", choices = "")
updateSelectizeInput(session, "split_SS", choices = "")
updateSelectizeInput(session, "split_CO", choices = "")
@@ -76,15 +77,15 @@ update_input <- function(eventName, rv, input, session){
updateSelectizeInput(session, "outCPdist", choices = "")
updateSelectizeInput(session, "outCPclass", choices = "")
updateSelectizeInput(session, "COdate", choices = "")
-
+ updateNumericInput(session, "gSearchInterval", value = NULL)
+ updateNumericInput(session, "gSearchMax", value = NULL)
}
if (eventName == "file_SE"){
updateSelectizeInput(session, "predsSE", choices = rv$colNames_SE_preds)
updateSelectizeInput(session, "obsSE", choices = rv$colNames_SE_obs)
updateSelectizeInput(session, "class", choices = rv$colNames_size,
- selected = rv$sizeCol
- )
+ selected = rv$sizeCol)
updateTabsetPanel(session, "LoadedDataViz", "Searcher Efficiency")
if (rv$nsizeclasses > 1){
@@ -93,7 +94,6 @@ update_input <- function(eventName, rv, input, session){
}
if (eventName == "file_SE_clear"){
-
toReset <- c("file_SE", "predsSE", "obsSE", "outSEp", "outSEk",
"outSEclass", "DWPCol", "split_SS", "split_CO",
"modelChoices_SE1", "outgclass")
@@ -121,8 +121,8 @@ update_input <- function(eventName, rv, input, session){
if (eventName == "file_CP"){
updateSelectizeInput(session, "predsCP", choices = rv$colNames_CP_preds)
- updateSelectizeInput(session, "ltp", choices = rv$colNames_CP_ltp)
- updateSelectizeInput(session, "fta", choices = rv$colNames_CP_fta)
+ updateSelectizeInput(session, "ltp", choices = rv$colNames_ltp)
+ updateSelectizeInput(session, "fta", choices = rv$colNames_fta)
updateSelectizeInput(session, "class", choices = rv$colNames_size,
selected = rv$sizeCol
)
@@ -159,13 +159,16 @@ update_input <- function(eventName, rv, input, session){
}
if (eventName == "file_SS"){
+ updateNumericInput(session, "gSearchInterval", value = rv$SS[["I"]])
+ updateNumericInput(session, "gSearchMax", value = rv$SS[["span"]])
+ updateSelectizeInput(session, "split_SS", choices = rv$splittable_SS)
updateTabsetPanel(session, "LoadedDataViz", "Search Schedule")
}
if (eventName == "file_SS_clear"){
toReset <- c("file_SS", "gSearchInterval", "gSearchMax",
- "split_SS", "split_CO", "outgclass")
+ "split_SS", "split_CO", "outgclass")
lapply(toReset, reset)
updateSelectizeInput(session, "split_SS", choices = "")
@@ -199,13 +202,11 @@ update_input <- function(eventName, rv, input, session){
if (eventName == "file_CO"){
updateSelectizeInput(session, "COdate", choices = rv$colNames_COdates)
if (length(rv$colNames_COdates) == 1){
- updateSelectizeInput(session, "COdate",
- choices = rv$colNames_COdates, selected = rv$colNames_COdates
- )
+ updateSelectizeInput(session, "COdate", choices = rv$colNames_COdates,
+ selected = rv$colNames_COdates)
}
- updateSelectizeInput(session, "sizeCol", choices = rv$colNames_size,
- selected = rv$sizeCol
- )
+ updateSelectizeInput(session, "class", choices = rv$colNames_size,
+ selected = rv$sizeCol)
updateTabsetPanel(session, "LoadedDataViz", "Carcass Observations")
}
@@ -230,14 +231,13 @@ update_input <- function(eventName, rv, input, session){
updateSelectizeInput(session, "predsSE", choices = rv$colNames_SE_preds)
updateSelectizeInput(session, "obsSE", choices = rv$colNames_SE_obs)
updateSelectizeInput(session, "class", choices = rv$colNames_size,
- selected = rv$sizeCol
- )
+ selected = rv$sizeCol)
+ updateNumericInput(session, "gSearchInterval", value = rv$SS[["I"]])
+ updateNumericInput(session, "gSearchMax", value = rv$SS[["span"]])
updateSelectizeInput(session, "predsCP", choices = rv$colNames_CP_preds)
- updateSelectizeInput(session, "ltp", choices = rv$colNames_CP_ltp)
- updateSelectizeInput(session, "fta", choices = rv$colNames_CP_fta)
- updateSelectizeInput(session, "class", choices = rv$colNames_size,
- selected = rv$sizeCol
- )
+ updateSelectizeInput(session, "ltp", choices = rv$colNames_ltp)
+ updateSelectizeInput(session, "fta", choices = rv$colNames_fta)
+ updateSelectizeInput(session, "class", choices = rv$colNames_size)
updateSelectizeInput(session, "DWPCol", choices = rv$colNames_DWP)
if (length(rv$colNames_DWP) == 1){
updateSelectizeInput(session, "DWPCol", selected = rv$colNames_DWP)
@@ -251,9 +251,10 @@ update_input <- function(eventName, rv, input, session){
choices = rv$colNames_COdates, selected = rv$colNames_COdates
)
}
- updateSelectizeInput(session, "sizeCol", choices = rv$colNames_size,
+ updateSelectizeInput(session, "class", choices = rv$colNames_size,
selected = rv$sizeCol
)
+
if (rv$nsizeclasses > 1){
updateSelectizeInput(session, "DWPCol", selected = " ")
}
@@ -269,8 +270,7 @@ update_input <- function(eventName, rv, input, session){
toReset <- c(
"outCPl", "outCPs", "outCPdist", "outsizeclassCP", "modelChoices_CP1",
"outSEp", "outSEk", "outsizeclassSE", "modelChoices_SE1",
- "DWPCol", "split_SS", "split_CO",
- "gSearchInterval", "gSearchMax", "outgclass")
+ "DWPCol", "split_SS", "split_CO", "outgclass")
lapply(toReset, reset)
@@ -351,23 +351,19 @@ update_input <- function(eventName, rv, input, session){
updateSelectizeInput(session, "outSEp", choices = rv$modNames_SEp)
updateSelectizeInput(session, "outSEk", choices = rv$modNames_SEk)
updateSelectizeInput(session, "outSEclass", choices = rv$sizeclasses)
-
updateSelectizeInput(session, "DWPCol", choices = rv$colNames_DWP,
selected = rv$DWPCol)
if (length(rv$colNames_DWP) == 1){
updateSelectizeInput(session, "DWPCol", selected = rv$colNames_DWP)
}
-
reset("outgclass")
- reset("gSearchInterval")
- reset("gSearchMax")
updateSelectizeInput(session, "split_SS", choices = "")
updateSelectizeInput(session, "split_CO", choices = "")
updateSelectizeInput(session, "outgclass", choices = "")
}
if (eventName == "run_SE_clear"){
- toReset <- c("outSEp", "outSEk", "outsizeclassSE", "DWPCol",
+ toReset <- c("outSEp", "outSEk", "outsizeclassSE", #DWPCol, # reset DWPCol after run_SE_clear?
"split_SS", "split_CO", "modelChoices_SE1", "outgclass")
lapply(toReset, reset)
updateSelectizeInput(session, "modelChoices_SE1", choices = "")
@@ -385,7 +381,7 @@ update_input <- function(eventName, rv, input, session){
}
if (eventName == "run_CP"){
- toReset <- c("outgclass", "gSearchInterval", "gSearchMax")
+ toReset <- c("outgclass")#, "gSearchInterval", "gSearchMax")
lapply(toReset, reset)
updateTabsetPanel(session, "analyses_CP", "Model Comparison")
updateSelectizeInput(session, "outCPl", choices = rv$modNames_CPl)
@@ -399,8 +395,7 @@ update_input <- function(eventName, rv, input, session){
if (eventName == "run_CP_clear"){
toReset <- c("outCPl", "outCPs", "outCPdist", "outsizeclassCP",
- "split_SS", "split_CO", "modelChoices_CP1", "outgclass",
- "gSearchInterval", "gSearchMax")
+ "split_SS", "split_CO", "modelChoices_CP1", "outgclass")
lapply(toReset, reset)
updateSelectizeInput(session, "modelChoices_CP1", choices = "")
updateSelectizeInput(session, "split_SS", choices = "")
@@ -418,13 +413,6 @@ update_input <- function(eventName, rv, input, session){
updateSelectizeInput(session, "outCPdist", choices = rv$modNames_CPdist)
}
- if (eventName == "useSSdata"){
- if (!is.na(rv$SStemp[1])){
- updateNumericInput(session, "gSearchInterval", value = rv$avgSI)
- updateNumericInput(session, "gSearchMax", value = max(rv$SS))
- }
- }
-
if (eventName == "run_g"){
updateSelectizeInput(session, "outgclass", choices = rv$sizeclasses_g)
updateTabsetPanel(session, "analyses_g", "Summary")
@@ -437,7 +425,7 @@ update_input <- function(eventName, rv, input, session){
if (eventName == "run_M"){
updateNumericInput(session, "frac", value = rv$frac)
- updateSelectizeInput(session, "split_SS", choices = rv$colNames_SS_nosel)
+ updateSelectizeInput(session, "split_SS", choices = rv$splittable_SS)
updateSelectizeInput(session, "split_CO", choices = rv$colNames_CO)
}
@@ -452,7 +440,7 @@ update_input <- function(eventName, rv, input, session){
if (eventName == "split_M_clear"){
reset("split_SS")
reset("split_CO")
- updateSelectizeInput(session, "split_SS", choices = rv$colNames_SS_nosel)
+ updateSelectizeInput(session, "split_SS", choices = rv$splittable_SS)
updateSelectizeInput(session, "split_CO", choices = rv$colNames_CO)
}
}
diff --git a/R/app_update_output.R b/R/app_update_output.R
index 1489b8079b33a4079377439c37dd72607927a8f9..8614ed9b9d0f822aa7289c15c2f18472f2a8f440 100644
--- a/R/app_update_output.R
+++ b/R/app_update_output.R
@@ -12,7 +12,7 @@
#' "outSEclass", "outSEp", "outSEk", "ltp", "fta", "predsCP", "run_CP",
#' "run_CP_clear", "outCPclass", "outCPdist", "outCPl", "outCPs",
#' "run_M", "run_M_clear", "split_M", "split_M_clear", "transpose_split",
-#' "useSSdata", "useSSinputs", "run_g", "run_g_clear", or "outgclass".
+#' "run_g", "run_g_clear", or "outgclass".
#'
#' @param rv Reactive values list for the GenEst GUI.
#'
@@ -31,8 +31,8 @@ update_output <- function(eventName, rv, output){
"outSEclass", "outSEp", "outSEk", "ltp", "fta", "predsCP",
"run_CP", "run_CP_clear", "outCPclass", "outCPdist",
"outCPl", "outCPs", "run_M", "run_M_clear", "split_M",
- "split_M_clear", "transpose_split", "useSSdata",
- "useSSinputs", "run_g", "run_g_clear", "outgclass",
+ "split_M_clear", "transpose_split",
+ "run_g", "run_g_clear", "outgclass",
"load_RP", "load_RPbat", "load_cleared", "load_PV",
"load_trough", "load_powerTower", "load_mock")
@@ -156,6 +156,7 @@ update_output <- function(eventName, rv, output){
"text_CP_est", "fig_M", "table_M", "MModDone", "table_g",
"fig_g", "gModDone", "sizeclass_gyn", "sizeclass_g1",
"sizeclass_g2")
+
output <- reNULL(output, toNULL)
output$kNeed <- setkNeed(rv)
@@ -167,7 +168,6 @@ update_output <- function(eventName, rv, output){
caption = paste0("File: ", rv$filename_CP)))
output$filename_CP <- renderText(paste0("File: ", rv$filename_CP))
- outputOptions(output, "data_SS", suspendWhenHidden = FALSE)
output$data_SS <- renderDTns(datatable(rv$data_SS,
caption = paste0("File: ", rv$filename_SS)))
output$filename_SS <- renderText(paste0("File: ", rv$filename_SS))
@@ -201,6 +201,8 @@ update_output <- function(eventName, rv, output){
selectedData <- selectData(rv$data_CP, selectedCols)
output$selected_CP <- renderDTns(datatable(selectedData))
}
+ output$sizeclasses_SE <- renderText(paste(rv$sizeclasses_SE, collapse = " "))
+ output$sizeclasses_CP <- renderText(paste(rv$sizeclasses_CP, collapse = " "))
output$kFixedInput <- kFixedWidget(rv$sizeclasses_k)
}
@@ -445,17 +447,15 @@ update_output <- function(eventName, rv, output){
}
}
- if (eventName == "useSSdata" | eventName == "useSSinputs"){
- output$SStext <- renderText(rv$SStext)
- }
if (eventName == "run_g"){
toNULL <- c("table_g", "fig_g", "gModDone", "sizeclass_gyn",
"sizeclass_g1", "sizeclass_g2")
output <- reNULL(output, toNULL)
- if (!is.null(rv$gGeneric[[1]])){
+ if (length(rv$gGeneric) > 0 && !is.null(rv$gGeneric[[1]])){
summaryTab <- summary(rv$gGeneric[[1]], CL = rv$CL)
- output$table_g <- renderDataTable(summaryTab)
+ output$table_g <- renderDataTable(summaryTab,
+ caption = paste0("I = ", rv$SS[["I"]], ", span = ", rv$SS[["span"]]))
output$fig_g <- renderPlot({
tryCatch(
plot(rv$gGeneric[[1]], CL = rv$CL),
@@ -503,9 +503,11 @@ update_output <- function(eventName, rv, output){
output$gModDone <- renderText("OK")
outputOptions(output, "gModDone", suspendWhenHidden = FALSE)
- preText <- paste0("Size class: ", rv$sizeclass_g)
+ preText <- paste0(
+ "Size class: ", rv$sizeclass_g, " ........ ",
+ "Search Schedule: I = ", round(rv$SS$I, 1), ", span = ", rv$SS$span)
if (length(rv$sizeclasses_g) == 1){
- preText <- ""
+ preText <- paste0("Search Schedule: I = ", round(rv$SS$I, 1), ", span = ", rv$SS$span)
}
scText <- renderText(preText)
output$sizeclass_g1 <- scText
@@ -604,7 +606,7 @@ update_output <- function(eventName, rv, output){
)
}, height = rv$figH_M, width = rv$figW_M
)
- output$dlMfig <- downloadMFig(rv, TRUE, TRUE)
+ output$dlMfig <- downloadMFig(rv, TRUE)
}
}
diff --git a/R/app_update_rv.R b/R/app_update_rv.R
index b58612726f7147f2e2f38ef4e0dcdb9bf88885ae..00cfa040f88edccecef8685e641969488cde3270 100644
--- a/R/app_update_rv.R
+++ b/R/app_update_rv.R
@@ -11,9 +11,8 @@
#' "outSEclass", "outSEp", "outSEk", "ltp", "fta", "predsCP", "run_CP",
#' "run_CP_clear", "outCPclass", "outCPdist", "outCPl", "outCPs",
#' "run_M", "run_M_clear", "split_M", "split_M_clear", "transpose_split",
-#' "useSSdata", "useSSinputs", "run_g", "run_g_clear", "outgclass", "load_RP",
-#' "load_RPbat", "load_cleared", "load_PV", "load_trough", "load_powerTower",
-#' or "load_mock"
+#' "run_g", "run_g_clear", "outgclass", "load_RP", "load_RPbat",
+#' "load_cleared", "load_PV", "load_trough", "load_powerTower", or "load_mock"
#'
#' @param rv Reactive values list for the GenEst GUI, created by
#' \code{\link{initialReactiveValues}}, which calls
@@ -33,8 +32,8 @@ update_rv <- function(eventName, rv, input){
"outSEclass", "outSEp", "outSEk", "ltp", "fta", "predsCP",
"run_CP", "run_CP_clear", "outCPclass", "outCPdist",
"outCPl", "outCPs", "run_M", "run_M_clear", "split_M",
- "split_M_clear", "transpose_split", "useSSdata",
- "useSSinputs", "run_g", "run_g_clear", "outgclass",
+ "split_M_clear", "transpose_split",
+ "run_g", "run_g_clear", "outgclass",
"load_RP", "load_RPbat", "load_cleared", "load_PV",
"load_trough", "load_powerTower", "load_mock")
@@ -51,8 +50,8 @@ update_rv <- function(eventName, rv, input){
"mods_SE_og", "sizeclasses_SE", "outSEpk", "AICcTab_SE",
"modOrder_SE", "modNames_SE", "modNames_SEp", "modNames_SEk",
"modSet_SE", "best_SE", "modTab_SE", "modTabPretty_SE",
- "modTabDL_SE", "SStemp", "avgSI", "colNames_ss_sel",
- "colNames_ss_nosel", "M", "Msplit", "unitCol", "sizeCol_M",
+ "modTabDL_SE", "SStemp", "avgSI", "splittable_SS",
+ "M", "Msplit", "unitCol", "sizeCol_M",
"split_CO", "split_SS", "SEmodToUse",
"sizeclasses_g", "nsizeclasses_g", "gGeneric", "SEmodToUse_g",
"data_CP", "filename_CP", "colNames_CP", "colNames_CP_preds",
@@ -68,19 +67,14 @@ update_rv <- function(eventName, rv, input){
"sizeclasses_g", "nsizeclasses_g", "gGeneric", "CPmodToUse_g",
"data_SS", "colNames_SS", "data_DWP", "colNames_DWP",
"data_CO", "colNames_CO", "colNames_COdates", "filename_SS",
- "filename_DWP", "filename_CO")
+ "filename_DWP", "filename_CO", "sizeCol", "colNames_size",
+ "colNames_size0")
rv <- reNULL(rv, toNULL)
toReVal <- c("nsplit_CO", "nsplit_SS", "figH_M", "figW_M", "figH_g",
- "figW_g", "figH_SE", "figW_SE", "SS", "SStext",
+ "figW_g", "figH_SE", "figW_SE", "SS",
"gSearchInterval", "gSearchMax", "figH_CP", "figW_CP")
rv <- reVal(rv, toReVal)
- rv$colNames_size <- updateColNames_size(rv)
- rv$colNames_size0 <- updateColNames_size(rv)
- rv$sizeCol <- updatesizeCol(input$class, rv$colNames_size)
- rv$toRemove_sizeCol <- c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta,
- rv$preds_CP)
- rv$colNames_size <- removeCols(rv$colNames_size0, rv$toRemove_sizeCol)
}
if (eventName == "file_SE"){
@@ -92,33 +86,28 @@ update_rv <- function(eventName, rv, input){
"mods_SE_og", "sizeclasses_SE", "outSEpk", "AICcTab_SE",
"modOrder_SE", "modNames_SE", "modNames_SEp", "modNames_SEk",
"modSet_SE", "best_SE", "modTab_SE", "modTabPretty_SE",
- "modTabDL_SE", "SStemp", "avgSI", "colNames_ss_sel",
- "colNames_ss_nosel", "M", "Msplit", "unitCol", "sizeCol_M",
+ "modTabDL_SE", "SStemp", "avgSI",
+ "M", "Msplit", "unitCol", "sizeCol_M",
"split_CO", "split_SS", "SEmodToUse", "sizeCol", "sizeclasses",
"sizeclasses_g", "nsizeclasses_g", "gGeneric", "SEmodToUse_g")
rv <- reNULL(rv, toNULL)
toReVal <- c("nsplit_CO", "nsplit_SS", "figH_M", "figW_M", "figH_g",
- "figW_g", "figH_SE", "figW_SE", "SS", "SStext",
- "gSearchInterval", "gSearchMax")
+ "figW_g", "figH_SE", "figW_SE", "SS")
rv <- reVal(rv, toReVal)
- rv$colNames_size <- updateColNames_size(rv)
- rv$colNames_size0 <- updateColNames_size(rv)
- rv$sizeCol <- updatesizeCol(input$class, rv$colNames_size)
- rv$toRemove_sizeCol <- c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta,
- rv$preds_CP)
- rv$colNames_size <- removeCols(rv$colNames_size0, rv$toRemove_sizeCol)
rv$data_SE <- readCSV(input$file_SE$datapath)
rv$filename_SE <- input$file_SE$name
rv$colNames_SE <- colnames(rv$data_SE)
- rv$colNames_SE_preds <- predsCols(rv$data_SE)
rv$colNames_SE_preds0 <- predsCols(rv$data_SE)
- rv$colNames_SE_obs <- obsCols_SE(rv$data_SE)
rv$colNames_SE_obs0 <- obsCols_SE(rv$data_SE)
- rv$colNames_size <- updateColNames_size(rv)
rv$colNames_size0 <- updateColNames_size(rv)
- rv$sizeCol <- NULL
+ rv$colNames_SE_preds <- rv$colNames_SE_preds0
+ rv$colNames_SE_obs <- rv$colNames_SE_obs0
+ rv$colNames_size <- removeCols(rv$colNames_size0,
+ c(rv$ltp, rv$fta, rv$preds_CP))
+ if (!is.null(rv$sizeCol) && !(rv$sizeCol %in% rv$colNames_size))
+ rv$sizeCol <- NULL
}
if (eventName == "file_SE_clear"){
@@ -130,64 +119,50 @@ update_rv <- function(eventName, rv, input){
"mods_SE_og", "sizeclasses_SE", "outSEpk", "AICcTab_SE",
"modOrder_SE", "modNames_SE", "modNames_SEp", "modNames_SEk",
"modSet_SE", "best_SE", "modTab_SE", "modTabPretty_SE",
- "modTabDL_SE", "SStemp", "avgSI", "colNames_ss_sel",
- "colNames_ss_nosel", "M", "Msplit", "unitCol", "sizeCol_M",
- "split_CO", "split_SS", "SEmodToUse",
+ "modTabDL_SE", "SStemp", "avgSI", "M", "Msplit", "unitCol",
+ "sizeCol_M", "split_CO", "split_SS", "SEmodToUse",
"sizeclasses_g", "nsizeclasses_g", "gGeneric", "SEmodToUse_g")
rv <- reNULL(rv, toNULL)
toReVal <- c("nsplit_CO", "nsplit_SS", "figH_M", "figW_M", "figH_g",
- "figW_g", "figH_SE", "figW_SE", "SS", "SStext",
- "gSearchInterval", "gSearchMax")
+ "figW_g", "figH_SE", "figW_SE")
rv <- reVal(rv, toReVal)
- rv$colNames_size <- updateColNames_size(rv)
rv$colNames_size0 <- updateColNames_size(rv)
- rv$sizeCol <- updatesizeCol(input$class, rv$colNames_size)
- rv$toRemove_sizeCol <- c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta,
- rv$preds_CP)
- rv$colNames_size <- removeCols(rv$colNames_size0, rv$toRemove_sizeCol)
+ rv$colNames_size <- rv$colNames_size0
}
if (eventName == "file_CP"){
toNULL <- c("data_CP", "filename_CP", "colNames_CP", "colNames_CP_preds",
"colNames_CP_preds0", "colNames_fta", "colNames_fta0",
- "colNames_ltp", "colNames_ltp0", "colNames_CP_preds",
+ "colNames_ltp", "colNames_ltp0",
"toRemove_fta", "toRemove_ltp", "toRemove_CP_preds",
"sizeclass_CP", "ltp", "fta", "preds_CP", "dist",
"predictors_CP", "formula_l", "formula_s", "mods_CP",
"mods_CP_og", "sizeclasses_CP", "AICcTab_CP", "modOrder_CP",
"modNames_CP", "modNames_CPl", "modNames_CPs",
"modNames_CPdist", "modSet_CP", "best_CP", "modTab_CP",
- "modTabPretty_CP", "modTabDL_CP", "SStemp", "avgSI",
- "colNames_ss_sel", "colNames_ss_nosel", "M", "Msplit",
- "unitCol", "sizeCol_M", "split_CO", "split_SS", "sizeclasses",
- "sizeCol",
- "CPmodToUse", "sizeclasses_g", "nsizeclasses_g", "gGeneric",
- "CPmodToUse_g")
+ "modTabPretty_CP", "modTabDL_CP", "SStemp", "avgSI", "M",
+ "Msplit", "unitCol", "sizeCol_M", "split_CO", "split_SS",
+ "sizeclasses", "sizeCol", "CPmodToUse", "sizeclasses_g",
+ "nsizeclasses_g", "gGeneric", "CPmodToUse_g")
rv <- reNULL(rv, toNULL)
- rv$toRemove_sizeCol <- c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta,
- rv$preds_CP)
- rv$colNames_size <- removeCols(rv$colNames_size0, rv$toRemove_sizeCol)
- rv$colNames_size <- updateColNames_size(rv)
- rv$colNames_size0 <- updateColNames_size(rv)
- rv$sizeCol <- updatesizeCol(input$class, rv$colNames_size)
toReVal <- c("nsplit_CO", "nsplit_SS", "figH_M", "figW_M", "figH_g",
- "figW_g", "figH_CP", "figW_CP", "SS", "SStext",
- "gSearchInterval", "gSearchMax")
+ "figW_g", "figH_CP", "figW_CP")
rv <- reVal(rv, toReVal)
rv$data_CP <- readCSV(input$file_CP$datapath)
rv$filename_CP <- input$file_CP$name
rv$colNames_CP <- colnames(rv$data_CP)
- rv$colNames_CP_preds <- predsCols(rv$data_CP)
rv$colNames_CP_preds0 <- predsCols(rv$data_CP)
- rv$colNames_fta <- obsCols_fta(rv$data_CP)
- rv$colNames_ltp <- obsCols_ltp(rv$data_CP)
rv$colNames_fta0 <- obsCols_fta(rv$data_CP)
rv$colNames_ltp0 <- obsCols_ltp(rv$data_CP)
- rv$colNames_size <- updateColNames_size(rv)
rv$colNames_size0 <- updateColNames_size(rv)
- rv$sizeCol <- NULL
+ rv$colNames_fta <- rv$colNames_fta0
+ rv$colNames_ltp <- rv$colNames_ltp0
+ rv$colNames_size <- removeCols(rv$colNames_size0,
+ c(rv$obsCols_SE, rv$preds_SE))
+ if (!is.null(rv$sizeCol) && !(rv$sizeCol %in% rv$colNames_size))
+ rv$sizeCol <- NULL
}
if (eventName == "file_CP_clear"){
@@ -200,47 +175,62 @@ update_rv <- function(eventName, rv, input){
"mods_CP_og", "sizeclasses_CP", "AICcTab_CP", "modOrder_CP",
"modNames_CP", "modNames_CPl", "modNames_CPs",
"modNames_CPdist", "modSet_CP", "best_CP", "modTab_CP",
- "modTabPretty_CP", "modTabDL_CP", "SStemp", "avgSI",
- "colNames_ss_sel", "colNames_ss_nosel", "M", "Msplit",
- "unitCol", "sizeCol_M", "split_CO", "split_SS",
+ "modTabPretty_CP", "modTabDL_CP", "SStemp", "avgSI", "M",
+ "Msplit", "unitCol", "sizeCol_M", "split_CO", "split_SS",
"CPmodToUse", "sizeclasses_g", "nsizeclasses_g", "gGeneric",
"CPmodToUse_g")
rv <- reNULL(rv, toNULL)
- rv$toRemove_sizeCol <- c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta,
- rv$preds_CP)
- rv$colNames_size <- removeCols(rv$colNames_size0, rv$toRemove_sizeCol)
+
rv$colNames_size <- updateColNames_size(rv)
- rv$colNames_size0 <- updateColNames_size(rv)
- rv$sizeCol <- updatesizeCol(input$class, rv$colNames_size)
toReVal <- c("nsplit_CO", "nsplit_SS", "figH_M", "figW_M", "figH_g",
- "figW_g", "figH_CP", "figW_CP", "SS", "SStext",
- "gSearchInterval", "gSearchMax")
+ "figW_g", "figH_CP", "figW_CP")
rv <- reVal(rv, toReVal)
+ rv$colNames_size0 <- updateColNames_size(rv)
+ rv$colNames_size <- rv$colNames_size0
}
if (eventName == "file_SS"){
- toNULL <- c("data_SS", "colNames_SS", "SStemp", "avgSI",
- "colNames_ss_sel", "colNames_ss_nosel", "M", "Msplit",
- "unitCol", "sizeCol_M", "split_CO", "split_SS",
+ toNULL <- c("data_SS", "colNames_SS", "SStemp", "avgSI", "splittable_SS",
+ "M", "Msplit", "unitCol", "sizeCol_M", "split_CO", "split_SS",
"sizeclasses_g", "nsizeclasses_g", "gGeneric", "SEmodToUse_g")
rv <- reNULL(rv, toNULL)
toReVal <- c("nsplit_CO", "nsplit_SS", "figH_M", "figW_M", "figH_g",
- "figW_g", "SS", "SStext", "gSearchInterval", "gSearchMax")
+ "figW_g", "SS", "gSearchInterval", "gSearchMax")
rv <- reVal(rv, toReVal)
rv$data_SS <- readCSV(input$file_SS$datapath)
rv$filename_SS <- input$file_SS$name
rv$colNames_SS <- colnames(rv$data_SS)
+ rv$splittable_SS <- rv$colNames_SS # candidates for splittable columns
+ badind <- NULL
+ for (ci in 1:length(rv$splittable_SS)){
+ if (is.numeric(rv$data_SS[ , rv$splittable_SS[ci]])){
+ badind <- c(badind, ci) # SS splits are categorical (or dates)
+ } else {
+ SSlev <- rv$data_SS[ , rv$splittable_SS[ci]]
+ if (length(unique(SSlev)) == 1 || # no levels to split on
+ min(diff(match(SSlev, unique(SSlev)))) < 0) # contiguous blocks
+ badind <- c(badind, ci)
+ }
+ }
+ if (length(badind) > 0) rv$splittable_SS <- rv$splittable_SS[-badind]
+ rv$SStemp <- tryCatch(averageSS(rv$data_SS), error = function(x){NA})
+ if (!is.na(rv$SStemp[1])){ # aveSS for default SS in estg (if possible)
+ rv$SS <- list("span" = max(rv$SStemp), "I" = rv$SStemp[2])
+ rv$avgSI <- mean(diff(rv$SStemp[-length(rv$SStemp)]))
+ } else {
+ rv$SS <- NULL # no default
+ }
}
if (eventName == "file_SS_clear"){
- toNULL <- c("data_SS", "filename_SS", "colNames_SS", "SStemp", "avgSI",
- "colNames_ss_sel", "colNames_ss_nosel", "M", "Msplit",
- "unitCol", "sizeCol_M", "split_CO", "split_SS",
- "sizeclasses_g", "nsizeclasses_g", "gGeneric", "SEmodToUse_g")
+ toNULL <- c("data_SS", "filename_SS", "colNames_SS", "SStemp", "avgSI", "M",
+ "Msplit", "splittable_SS", "unitCol", "sizeCol_M", "split_CO",
+ "split_SS", "SS", "sizeclasses_g", "nsizeclasses_g", "gGeneric",
+ "SEmodToUse_g")
rv <- reNULL(rv, toNULL)
toReVal <- c("nsplit_CO", "nsplit_SS", "figH_M", "figW_M", "figH_g",
- "figW_g", "SS", "SStext", "gSearchInterval", "gSearchMax")
+ "figW_g", "gSearchInterval", "gSearchMax")
rv <- reVal(rv, toReVal)
}
@@ -265,25 +255,21 @@ update_rv <- function(eventName, rv, input){
}
if (eventName == "file_CO"){
-
- toNULL <- c("data_CO", "filename_CO", "colNames_CO", "colNames_COdates", "M", "Msplit",
- "unitCol", "sizeCol_M", "SEmodToUse", "split_CO", "split_SS")
+ toNULL <- c("data_CO", "filename_CO", "colNames_CO", "colNames_COdates",
+ "M", "Msplit", "unitCol", "sizeCol_M", "SEmodToUse", "split_CO",
+ "split_SS")
rv <- reNULL(rv, toNULL)
- rv$colNames_size <- updateColNames_size(rv)
- rv$colNames_size0 <- updateColNames_size(rv)
- rv$sizeCol <- updatesizeCol(input$class, rv$colNames_size)
- rv$toRemove_sizeCol <- c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta,
- rv$preds_CP)
- rv$colNames_size <- removeCols(rv$colNames_size0, rv$toRemove_sizeCol)
toReVal <- c("nsplit_CO", "nsplit_SS", "figH_M", "figW_M")
rv <- reVal(rv, toReVal)
rv$data_CO <- readCSV(input$file_CO$datapath)
rv$filename_CO <- input$file_CO$name
rv$colNames_CO <- colnames(rv$data_CO)
rv$colNames_COdates <- dateCols(rv$data_CO)
- rv$colNames_size <- updateColNames_size(rv)
rv$colNames_size0 <- updateColNames_size(rv)
- rv$sizeCol <- updatesizeCol(input$class, rv$colNames_size)
+ rv$colNames_size <- removeCols(rv$colNames_size0,
+ c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta, rv$preds_CP))
+ if (!is.null(rv$sizeCol) && !(rv$sizeCol %in% rv$colNames_size))
+ rv$sizeCol <- NULL
}
if (eventName == "file_CO_clear"){
@@ -291,13 +277,10 @@ update_rv <- function(eventName, rv, input){
"unitCol", "sizeCol_M", "SEmodToUse", "split_CO", "split_SS")
rv <- reNULL(rv, toNULL)
rv$colNames_size <- updateColNames_size(rv)
- rv$colNames_size0 <- updateColNames_size(rv)
- rv$sizeCol <- updatesizeCol(input$class, rv$colNames_size)
- rv$toRemove_sizeCol <- c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta,
- rv$preds_CP)
- rv$colNames_size <- removeCols(rv$colNames_size0, rv$toRemove_sizeCol)
toReVal <- c("nsplit_CO", "nsplit_SS", "figH_M", "figW_M")
rv <- reVal(rv, toReVal)
+ rv$colNames_size0 <- updateColNames_size(rv)
+ rv$colNames_size <- rv$colNames_size0
}
if (grepl("load_", eventName)){
@@ -309,16 +292,15 @@ update_rv <- function(eventName, rv, input){
"mods_SE_og", "sizeclasses_SE", "outSEpk", "AICcTab_SE",
"modOrder_SE", "modNames_SE", "modNames_SEp", "modNames_SEk",
"modSet_SE", "best_SE", "modTab_SE", "modTabPretty_SE",
- "modTabDL_SE", "SStemp", "avgSI", "colNames_ss_sel",
- "colNames_ss_nosel", "M", "Msplit", "unitCol", "sizeCol_M",
- "split_CO", "split_SS", "SEmodToUse",
- "sizeclasses_g", "nsizeclasses_g", "gGeneric", "SEmodToUse_g",
- "data_CP", "filename_CP", "colNames_CP", "colNames_CP_preds",
- "colNames_CP_preds0", "colNames_fta", "colNames_fta0",
- "colNames_ltp", "colNames_ltp0", "colNames_CP_preds",
- "toRemove_fta", "toRemove_ltp", "toRemove_CP_preds",
- "sizeclass_CP", "ltp", "fta", "preds_CP", "dist",
- "predictors_CP", "formula_l", "formula_s", "mods_CP",
+ "modTabDL_SE", "SStemp", "avgSI", "M", "Msplit", "unitCol",
+ "sizeCol_M", "split_CO", "split_SS", "SEmodToUse",
+ "splittable_SS", "sizeclasses_g", "nsizeclasses_g", "gGeneric",
+ "SEmodToUse_g", "data_CP", "filename_CP", "colNames_CP",
+ "colNames_CP_preds", "colNames_CP_preds0", "colNames_fta",
+ "colNames_fta0", "colNames_ltp", "colNames_ltp0",
+ "colNames_CP_preds", "toRemove_fta", "toRemove_ltp",
+ "toRemove_CP_preds", "sizeclass_CP", "ltp", "fta", "preds_CP",
+ "dist", "predictors_CP", "formula_l", "formula_s", "mods_CP",
"mods_CP_og", "sizeclasses_CP", "AICcTab_CP", "modOrder_CP",
"modNames_CP", "modNames_CPl", "modNames_CPs",
"modNames_CPdist", "modSet_CP", "best_CP", "modTab_CP",
@@ -330,7 +312,7 @@ update_rv <- function(eventName, rv, input){
rv <- reNULL(rv, toNULL)
toReVal <- c("nsplit_CO", "nsplit_SS", "figH_M", "figW_M", "figH_g",
- "figW_g", "figH_SE", "figW_SE", "SS", "SStext",
+ "figW_g", "figH_SE", "figW_SE", "SS",
"gSearchInterval", "gSearchMax", "figH_CP", "figW_CP")
rv <- reVal(rv, toReVal)
@@ -364,7 +346,17 @@ update_rv <- function(eventName, rv, input){
rv$data_SS <- dataset$SS
rv$filename_SS <- paste0(dataName, "$SS")
-
+ rv$SStemp <- tryCatch(averageSS(rv$data_SS), error = function(x){NA})
+ if (!is.na(rv$SStemp[1]) && length(rv$SStemp > 1)){
+ rv$avgSI <- mean(diff(rv$SStemp[-length(rv$SStemp)]))
+ if (max(rv$SStemp)%%rv$SStemp[2] == 0){
+ rv$SS <- list("span" = max(rv$SStemp), "I" = rv$SStemp[2])
+ } else {
+ rv$SS <- list("span" = SStmp[2] * length(rv$SStemp), "I" = rv$SStemp[2])
+ }
+ } else {
+ rv$SS <- NULL
+ }
rv$data_DWP <- dataset$DWP
rv$filename_DWP <- paste0(dataName, "$DWP")
@@ -375,70 +367,81 @@ update_rv <- function(eventName, rv, input){
rv$colNames_COdates <- dateCols(rv$data_CO)
rv$colNames_SS <- colnames(rv$data_SS)
+ rv$splittable_SS <- rv$colNames_SS
+ badind <- NULL
+ for (ci in 1:length(rv$splittable_SS)){
+ if (is.numeric(rv$data_SS[ , rv$splittable_SS[ci]])){
+ badind <- c(badind, ci)
+ } else {
+ SSlev <- rv$data_SS[ , rv$splittable_SS[ci]]
+ if (min(diff(match(SSlev, unique(SSlev)))) < 0)
+ badind <- c(badind, ci)
+ }
+ }
+ if (length(badind) > 0) rv$splittable_SS <- rv$splittable_SS[-badind]
rv$colNames_DWP <- DWPCols(rv$data_DWP)
rv$colNames_CP <- colnames(rv$data_CP)
- rv$colNames_CP_preds <- predsCols(rv$data_CP)
rv$colNames_CP_preds0 <- predsCols(rv$data_CP)
- rv$colNames_fta <- obsCols_fta(rv$data_CP)
- rv$colNames_ltp <- obsCols_ltp(rv$data_CP)
rv$colNames_fta0 <- obsCols_fta(rv$data_CP)
rv$colNames_ltp0 <- obsCols_ltp(rv$data_CP)
+ rv$colNames_CP_preds <- rv$colNames_CP_preds0
+ rv$colNames_fta <- rv$colNames_fta0
+ rv$colNames_ltp <- rv$colNames_ltp0
rv$colNames_SE <- colnames(rv$data_SE)
- rv$colNames_SE_preds <- predsCols(rv$data_SE)
rv$colNames_SE_preds0 <- predsCols(rv$data_SE)
- rv$colNames_SE_obs <- obsCols_SE(rv$data_SE)
rv$colNames_SE_obs0 <- obsCols_SE(rv$data_SE)
+ rv$colNames_SE_preds <- rv$colNames_SE_preds0
+ rv$colNames_SE_obs <- rv$colNames_SE_obs0
-
- rv$colNames_size <- updateColNames_size(rv)
rv$colNames_size0 <- updateColNames_size(rv)
- rv$sizeCol <- updatesizeCol(input$class, rv$colNames_size)
-
- rv$colNames_SE_obs <- removeCols(rv$colNames_SE_obs, rv$sizeCol)
- rv$colNames_SE_preds <- removeCols(rv$colNames_SE_preds, rv$sizeCol)
- rv$colNames_CP_fta <- removeCols(rv$colNames_fta, rv$sizeCol)
- rv$colNames_CP_ltp <- removeCols(rv$colNames_ltp, rv$sizeCol)
- rv$colNames_CP_preds <- removeCols(rv$colNames_CP_preds, rv$sizeCol)
-
- rv$colNames_size <- updateColNames_size(rv)
- rv$colNames_size0 <- updateColNames_size(rv)
- rv$colNames_size <- updateColNames_size(rv)
- rv$colNames_size0 <- updateColNames_size(rv)
- rv$sizeCol <- NULL #updatesizeCol(input$class, rv$colNames_size)
-
+ rv$colNames_size <- rv$colNames_size0
+ rv$sizeCol <- NULL
}
if (eventName == "class"){
rv$sizeCol <- input$class
+##############
rv$obsCols_SE <- input$obsSE
rv$preds_SE <- input$predsSE
rv$ltp <- input$ltp
rv$fta <- input$fta
rv$preds_CP <- input$predsCP
- rv$toRemove_SE_preds <- c(rv$obsCols_SE, rv$sizeCol)
- rv$colNames_SE_preds <- removeCols(rv$colNames_SE_preds0, rv$toRemove_SE_preds)
- rv$toRemove_SE_obs <- c(rv$preds_SE, rv$sizeCol)
- rv$colNames_SE_obs <- removeCols(rv$colNames_SE_obs0, rv$toRemove_SE_obs)
- rv$toRemove_ltp <- c(rv$preds_CP, rv$fta, rv$sizeCol)
- rv$colNames_ltp <- removeCols(rv$colNames_ltp0, rv$toRemove_ltp)
- rv$toRemove_fta <- c(rv$preds_CP, rv$ltp, rv$sizeCol)
- rv$colNames_fta <- removeCols(rv$colNames_fta0, rv$toRemove_fta)
- rv$toRemove_CP_preds <- c(rv$ltp, rv$fta, rv$sizeCol)
+##############
+ #remove sizeCol from list of possibilities for SE_preds and SE_obs
+ rv$colNames_SE_preds <- removeCols(rv$colNames_SE_preds0,
+ c(rv$obsCols_SE, rv$sizeCol))
+ rv$colNames_SE_obs <- removeCols(rv$colNames_SE_obs0,
+ c(rv$preds_SE, rv$sizeCol))
+
+ #remove sizeCol from list of possibilities for CP_preds, ltp, and fta
rv$colNames_CP_preds <- removeCols(rv$colNames_CP_preds0,
- rv$toRemove_CP_preds)
+ c(rv$ltp, rv$fta, rv$sizeCol))
+ rv$colNames_ltp <- removeCols(rv$colNames_ltp0,
+ c(rv$preds_CP, rv$fta, rv$sizeCol))
+ rv$colNames_fta <- removeCols(rv$colNames_fta0,
+ c(rv$preds_CP, rv$ltp, rv$sizeCol))
+
if (!is.null(rv$sizeCol)){
- sizeclasses <- sort(unique(
- c(rv$data_SE[ , rv$sizeCol], rv$data_CP[ , rv$sizeCol])
- ))
+# sizeclasses <- sort(unique(
+# c(rv$data_SE[ , rv$sizeCol], rv$data_CP[ , rv$sizeCol])))
+ rv$sizeclasses <- sort(unique(
+ c(rv$data_SE[ , rv$sizeCol], rv$data_CP[ , rv$sizeCol])))
+ rv$sizeclasses_k <- sort(unique(rv$data_SE[ , rv$sizeCol]))
+ rv$sizeclasses_CP <- sort(unique(rv$data_CP[, rv$sizeCol]))
+ rv$sizeclasses_SE <- sort(unique(rv$data_SE[, rv$sizeCol]))
} else {
- sizeclasses <- NULL
+# sizeclasses <- NULL # why no rv$?
+ rv$sizeclasses <- NULL
+ rv$sizeclasses_k <- NULL
+# rv$sizeclasses_CP <- NULL
+# rv$sizeclasses_SE <- NULL
}
- rv$nsizeclasses <- length(sizeclasses)
- rv$sizeclasses_k <- sort(unique(rv$data_SE[ , rv$sizeCol]))
+# rv$nsizeclasses <- length(sizeclasses)
+ rv$nsizeclasses <- length(rv$sizeclasses)
rv$nsizeclasses_k <- length(rv$sizeclasses_k)
- if (rv$nsizeclasses > 1 & is.null(rv$DWPCol)){
- rv$DWPCol <- sizeclasses[1]
- }
+ tmp <- rv$DWPCol
+ if (rv$nsizeclasses > 1 & (is.null(tmp) || !(tmp %in% rv$sizeclasses)))
+ rv$DWPCol <- rv$sizeclasses[1]
if (rv$nsizeclasses == 0){
rv$sizeclasses_k <- ""
rv$nsizeclasses_k <- 1
@@ -447,21 +450,13 @@ update_rv <- function(eventName, rv, input){
}
if (eventName == "obsSE"){
- rv$sizeCol <- input$class
rv$obsCols_SE <- input$obsSE
- rv$preds_SE <- input$predsSE
- rv$ltp <- input$ltp
- rv$fta <- input$fta
- rv$preds_CP <- input$predsCP
- rv$toRemove_SE_preds <- c(rv$obsCols_SE, rv$sizeCol)
rv$colNames_SE_preds <- removeCols(rv$colNames_SE_preds0,
- rv$toRemove_SE_preds)
- rv$toRemove_sizeCol <- c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta,
- rv$preds_CP)
- rv$colNames_size <- removeCols(rv$colNames_size0, rv$toRemove_sizeCol)
+ c(rv$obsCols_SE, rv$sizeCol))
+ rv$colNames_size <- removeCols(rv$colNames_size0,
+ c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta, rv$preds_CP))
}
-
if (eventName == "predsSE"){
rv$sizeCol <- input$class
rv$obsCols_SE <- input$obsSE
@@ -469,63 +464,40 @@ update_rv <- function(eventName, rv, input){
rv$ltp <- input$ltp
rv$fta <- input$fta
rv$preds_CP <- input$predsCP
- rv$toRemove_SE_obs <- c(rv$preds_SE, rv$sizeCol)
- rv$colNames_SE_obs <- removeCols(rv$colNames_SE_obs0, rv$toRemove_SE_obs)
- rv$toRemove_sizeCol <- c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta,
- rv$preds_CP)
- rv$colNames_size <- removeCols(rv$colNames_size0, rv$toRemove_sizeCol)
+ rv$colNames_SE_obs <- removeCols(rv$colNames_SE_obs,
+ c(rv$preds_SE, rv$sizeCol))
+ rv$colNames_size <- removeCols(rv$colNames_size0,
+ c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta, rv$preds_CP))
}
-
if (eventName == "ltp"){
- rv$sizeCol <- input$class
- rv$obsCols_SE <- input$obsSE
- rv$preds_SE <- input$predsSE
rv$ltp <- input$ltp
- rv$fta <- input$fta
- rv$preds_CP <- input$predsCP
- rv$toRemove_fta <- c(rv$preds_CP, rv$ltp, rv$sizeCol)
- rv$toRemove_CP_preds <- c(rv$ltp, rv$fta, rv$sizeCol)
- rv$colNames_fta <- removeCols(rv$colNames_fta0, rv$toRemove_fta)
+ rv$colNames_fta <- removeCols(rv$colNames_fta0,
+ c(rv$preds_CP, rv$ltp, rv$sizeCol))
rv$colNames_CP_preds <- removeCols(rv$colNames_CP_preds0,
- rv$toRemove_CP_preds)
- rv$toRemove_sizeCol <- c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta,
- rv$preds_CP)
- rv$colNames_size <- removeCols(rv$colNames_size0, rv$toRemove_sizeCol)
+ c(rv$ltp, rv$fta, rv$sizeCol))
+ rv$colNames_size <- removeCols(rv$colNames_size0,
+ c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta, rv$preds_CP))
}
if (eventName == "fta"){
- rv$sizeCol <- input$class
- rv$obsCols_SE <- input$obsSE
- rv$preds_SE <- input$predsSE
- rv$ltp <- input$ltp
rv$fta <- input$fta
- rv$preds_CP <- input$predsCP
- rv$toRemove_ltp <- c(rv$preds_CP, rv$fta, rv$sizeCol)
- rv$toRemove_CP_preds <- c(rv$ltp, rv$fta, rv$sizeCol)
- rv$colNames_ltp <- removeCols(rv$colNames_ltp0, rv$toRemove_ltp)
+ rv$colNames_ltp <- removeCols(rv$colNames_ltp0,
+ c(rv$preds_CP, rv$fta, rv$sizeCol))
rv$colNames_CP_preds <- removeCols(rv$colNames_CP_preds0,
- rv$toRemove_CP_preds)
- rv$toRemove_sizeCol <- c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta,
- rv$preds_CP)
- rv$colNames_size <- removeCols(rv$colNames_size0, rv$toRemove_sizeCol)
+ c(rv$ltp, rv$fta, rv$sizeCol))
+ rv$colNames_size <- removeCols(rv$colNames_size0,
+ c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta, rv$preds_CP))
}
-
if (eventName == "predsCP"){
- rv$sizeCol <- input$class
- rv$obsCols_SE <- input$obsE
- rv$preds_SE <- input$predsSE
- rv$ltp <- input$ltp
- rv$fta <- input$fta
rv$preds_CP <- input$predsCP
- rv$toRemove_ltp <- c(rv$preds_CP, rv$fta, rv$sizeCol)
- rv$colNames_ltp <- removeCols(rv$colNames_ltp0, rv$toRemove_ltp)
- rv$toRemove_fta <- c(rv$preds_CP, rv$ltp, rv$sizeCol)
- rv$colNames_fta <- removeCols(rv$colNames_fta0, rv$toRemove_fta)
- rv$toRemove_sizeCol <- c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta,
- rv$preds_CP)
- rv$colNames_size <- removeCols(rv$colNames_size0, rv$toRemove_sizeCol)
+ rv$colNames_ltp <- removeCols(rv$colNames_ltp0,
+ c(rv$preds_CP, rv$fta, rv$sizeCol))
+ rv$colNames_fta <- removeCols(rv$colNames_fta0,
+ c(rv$preds_CP, rv$ltp, rv$sizeCol))
+ rv$colNames_size <- removeCols(rv$colNames_size0,
+ c(rv$obsCols_SE, rv$preds_SE, rv$ltp, rv$fta, rv$preds_CP))
}
@@ -536,21 +508,21 @@ update_rv <- function(eventName, rv, input){
"modSet_SE", "best_SE", "modTab_SE",
"modTabPretty_SE", "modTabDL_SE", "M", "Msplit", "unitCol",
"sizeCol_M", "SEmodToUse", "split_CO", "split_SS",
- "SStemp", "avgSI", "colNames_SS_sel", "colNames_SS_nosel",
- "sizeclasses_g", "nsizeclasses_g", "gGeneric",
- "SEmodToUse_g")
+ "SStemp", "avgSI", "sizeclasses_g", "nsizeclasses_g",
+ "gGeneric", "SEmodToUse_g")
rv <- reNULL(rv, toNULL)
- toReVal <- c("nsplit_CO", "nsplit_SS", "SS", "gSearchInterval",
- "gSearchMax", "SStext", "figH_SE", "figW_SE", "figH_M",
+ toReVal <- c("nsplit_CO", "nsplit_SS", "figH_SE", "figW_SE", "figH_M",
"figW_M", "figH_g", "figW_g")
rv <- reVal(rv, toReVal)
rv$kFixed <- numeric(rv$nsizeclasses_k) + NA
- names(rv$kFixed) <- rv$sizeclasses_k
- for (sci in rv$sizeclasses_k){
- rv$kFixed[sci] <- input[[paste0("kFixed_val_", sci)]]
+ if (length(rv$kFixed) > 1) {
+ names(rv$kFixed) <- rv$sizeclasses_k
+ for (sci in rv$sizeclasses_k){
+ rv$kFixed[sci] <- input[[paste0("kFixed_val_", sci)]]
+ }
+ } else {
+ rv$kFixed <- input[["kFixed_val_"]]
}
- rv$kFixed[!is.na(rv$kFixed) & (rv$kFixed > 1 | rv$kFixed < 0)] <- NA
-
rv$obsCols_SE <- input$obsSE
rv$preds_SE <- input$predsSE
rv$predictors_SE <- prepPredictors(rv$preds_SE)
@@ -559,7 +531,6 @@ update_rv <- function(eventName, rv, input){
rv$CL <- input$CL
rv$sizeCol <- input$class
-
rv$mods_SE <- suppressWarnings(
pkmSize(formula_p = rv$formula_p,
formula_k = rv$formula_k, data = rv$data_SE,
@@ -589,6 +560,7 @@ update_rv <- function(eventName, rv, input){
rv$figH_SE <- setFigH(rv$modSet_SE)
rv$figW_SE <- setFigW(rv$modSet_SE)
}
+ rv$outSEpk <- modNamePaste(c(input$outSEp, input$outSEk))
}
@@ -596,15 +568,12 @@ update_rv <- function(eventName, rv, input){
toNULL <- c("predictors_SE", "formula_p", "formula_k", "outSEpk",
"mods_SE", "mods_SE_og", "sizeclasses_SE", "AICcTab_SE",
"modOrder_SE", "modNames_SE", "modNames_SEp", "modNames_SEk",
- "modSet_SE", "best_SE", "modTab_SE",
- "modTabPretty_SE", "modTabDL_SE", "M", "Msplit", "unitCol",
- "sizeCol_M", "SEmodToUse", "split_CO", "split_SS",
- "SStemp", "avgSI", "colNames_SS_sel", "colNames_SS_nosel",
- "sizeclasses_g", "nsizeclasses_g", "gGeneric",
- "SEmodToUse_g")
+ "modSet_SE", "best_SE", "modTab_SE", "modTabPretty_SE",
+ "modTabDL_SE", "M", "Msplit", "unitCol", "sizeCol_M",
+ "SEmodToUse", "split_CO", "split_SS", "SStemp", "avgSI",
+ "sizeclasses_g", "nsizeclasses_g", "gGeneric", "SEmodToUse_g")
rv <- reNULL(rv, toNULL)
- toReVal <- c("nsplit_CO", "nsplit_SS", "SS", "gSearchInterval",
- "gSearchMax", "SStext", "figH_SE", "figW_SE", "figH_M",
+ toReVal <- c("nsplit_CO", "nsplit_SS", "figH_SE", "figW_SE", "figH_M",
"figW_M", "figH_g", "figW_g")
rv <- reVal(rv, toReVal)
@@ -656,13 +625,11 @@ update_rv <- function(eventName, rv, input){
"modOrder_CP", "modNames_CP", "modNames_CPl", "modNames_CPs",
"modNames_CPdist", "modSet_CP", "best_CP", "modTab_CP",
"modTabPretty_CP", "modTabDL_CP", "M", "Msplit", "unitCol",
- "sizeCol_M", "SEmodToUse", "split_CO", "split_SS",
- "SStemp", "avgSI", "colNames_SS_sel", "colNames_SS_nosel",
- "sizeclasses_g", "nsizeclasses_g", "gGeneric",
+ "sizeCol_M", "SEmodToUse", "split_CO", "split_SS", "SStemp",
+ "avgSI", "sizeclasses_g", "nsizeclasses_g", "gGeneric",
"SEmodToUse_g")
rv <- reNULL(rv, toNULL)
- toReVal <- c("nsplit_CO", "nsplit_SS", "SS", "gSearchInterval",
- "gSearchMax", "SStext", "figH_CP", "figW_CP", "figH_M",
+ toReVal <- c("nsplit_CO", "nsplit_SS", "figH_CP", "figW_CP", "figH_M",
"figW_M", "figH_g", "figW_g")
rv <- reVal(rv, toReVal)
rv$ltp <- input$ltp
@@ -723,12 +690,10 @@ update_rv <- function(eventName, rv, input){
"modNames_CPdist", "modSet_CP", "best_CP", "modTab_CP",
"modTabPretty_CP", "modTabDL_CP", "M", "Msplit", "unitCol",
"sizeCol_M", "SEmodToUse", "split_CO", "split_SS",
- "SStemp", "avgSI", "colNames_SS_sel", "colNames_SS_nosel",
- "sizeclasses_g", "nsizeclasses_g", "gGeneric",
- "SEmodToUse_g")
+ "SStemp", "avgSI", "sizeclasses_g", "nsizeclasses_g",
+ "gGeneric", "SEmodToUse_g")
rv <- reNULL(rv, toNULL)
- toReVal <- c("nsplit_CO", "nsplit_SS", "SS", "gSearchInterval",
- "gSearchMax", "SStext", "figH_CP", "figW_CP", "figH_M",
+ toReVal <- c("nsplit_CO", "nsplit_SS", "figH_CP", "figW_CP", "figH_M",
"figW_M", "figH_g", "figW_g")
rv <- reVal(rv, toReVal)
@@ -784,39 +749,6 @@ update_rv <- function(eventName, rv, input){
}
}
- if (eventName == "useSSdata"){
- rv$SS <- NULL
- rv$SStemp <- tryCatch(averageSS(rv$data_SS), error = function(x){NA})
- if (!is.na(rv$SStemp[1])){
- rv$SS <- rv$SStemp
- rv$avgSI <- mean(diff(rv$SS[-length(rv$SS)]))
- rv$SStext <- paste(rv$SS, collapse = ", ")
- }
- }
-
- if (eventName == "useSSinputs"){
- rv$SStemp <- NA
- rv$gSearchInterval <- input$gSearchInterval
- rv$gSearchMax <- input$gSearchMax
- if (rv$gSearchInterval > 0 & rv$gSearchMax > 0){
- rv$SStemp <- seq(0, rv$gSearchMax, by = rv$gSearchInterval)
- }
- if (rv$gSearchMax %% 1 != 0){
- rv$SStemp <- NA
- }
- if (any(is.na(rv$SStemp)) || any(rv$SStemp < 0) |
- any(rv$SStemp %% 1 != 0)){
- rv$SStemp <- NA
- }
- if (!is.na(rv$SStemp[1])){
- rv$SS <- seq(0, rv$gSearchMax, by = rv$gSearchInterval)
- if (max(rv$SS) != rv$gSearchMax){
- rv$SS <- c(rv$SS, rv$gSearchMax)
- }
- rv$SStext <- paste(rv$SS, collapse = ", ")
- }
- }
-
if (eventName == "run_g"){
rv$CL <- input$CL
toNULL <- c("sizeclasses_g", "gGeneric", "SEmodToUse_g")
@@ -844,12 +776,22 @@ update_rv <- function(eventName, rv, input){
rv$nsizeclasses_g <- 1
}
}
-
rv$nsim <- input$nsim
rv$gGeneric <- list()
for (sci in 1:length(rv$sizeclasses_g)){
+ if (is.null(input[[paste0("modelChoices_SE", sci)]])){
+ showNotification(paste0("No SE model selected for ",
+ rv$sizeclasses[sci], "...error."), type = "error")
+ return(rv)
+ }
+ if (is.null(input[[paste0("modelChoices_CP", sci)]])){
+ showNotification(paste0("No CP model selected for ",
+ rv$sizeclasses[sci], "...error."), type = "error")
+ return(rv)
+ }
rv$SEmodToUse_g <- input[[paste0("modelChoices_SE", sci)]]
rv$CPmodToUse_g <- input[[paste0("modelChoices_CP", sci)]]
+
rv$SEmodToUse_g <- gsub("~ constant", "~ 1", rv$SEmodToUse_g)
rv$CPmodToUse_g <- gsub("~ constant", "~ 1", rv$CPmodToUse_g)
@@ -857,9 +799,22 @@ update_rv <- function(eventName, rv, input){
rv$CPmodToUse_g <- paste(rv$CPmodToUse_g, "; NULL", sep = "")
}
rv$CPmodToUse_g <- paste("dist: ", rv$CPmodToUse_g, sep = "")
+ rv$SS <- list("span" = input[["gSearchMax"]], "I" = input[["gSearchInterval"]])
+ if (is.null(rv$SS[["span"]]) || is.null(rv$SS[["I"]]) ||
+ is.na(rv$SS[["span"]]) || is.na(rv$SS[["span"]])){
+ showNotification("Enter search interval and span before estimating g",
+ type = "error")
+ return(rv)
+ }
+ if (rv$SS[["span"]] < 2 * rv$SS[["I"]]){
+ days <- c(0, rv$SS[["I"]])
+ } else {
+ days <- tryCatch(seq(0, rv$SS[["span"]], by = rv$SS[["I"]]), error = function(x) NULL)
+ }
+ if (is.null(days)) return(rv)
rv$gGeneric[[sci]] <- tryCatch(
- estgGeneric(nsim = rv$nsim, days = rv$SS,
+ estgGeneric(nsim = rv$nsim, days = days,
model_SE = rv$mods_SE[[sci]][[rv$SEmodToUse_g]],
model_CP = rv$mods_CP[[sci]][[rv$CPmodToUse_g]]
),
@@ -883,8 +838,7 @@ update_rv <- function(eventName, rv, input){
}
if (eventName == "run_M"){
-
- toNULL <- c("M", "Msplit", "unitCol", "sizeCol_M", "SEmodToUse",
+ toNULL <- c("M", "Msplit", "unitCol", "sizeCol_M", "SEmodToUse",
"split_CO", "split_SS")
rv <- reNULL(rv, toNULL)
toReVal <- c("nsplit_CO", "nsplit_SS", "figH_M", "figW_M")
@@ -900,7 +854,6 @@ update_rv <- function(eventName, rv, input){
return(rv)
}
}
-
rv$nsizeclasses <- length(rv$sizeclasses)
if (length(rv$nsizeclasses) == 1){
if (is.null(rv$sizeclasses)){
@@ -910,29 +863,35 @@ update_rv <- function(eventName, rv, input){
rv$COdate <- input$COdate
rv$nsim <- input$nsim
rv$frac <- input$frac
- if (rv$frac < 0.01 | rv$frac > 1){
- return(rv)
- }
-
+ if (rv$frac < 0.01 | rv$frac > 1) return(rv)
rv$SEmodToUse <- rep(NA, rv$nsizeclasses)
rv$CPmodToUse <- rep(NA, rv$nsizeclasses)
- if (length(rv$SEmodToUse) != length(rv$CPmodToUse)){
- return(rv)
- }
names(rv$SEmodToUse) <- rv$sizeclasses
names(rv$CPmodToUse) <- rv$sizeclasses
for (sci in 1:length(rv$sizeclasses)){
+ if (is.null(input[[paste0("modelChoices_SE", sci)]])){
+ showNotification(paste0("No SE model selected for ",
+ rv$sizeclasses[sci], "...error."), type = "error")
+ return(rv)
+ }
rv$SEmodToUse[sci] <- input[[paste0("modelChoices_SE", sci)]]
+ if (is.null(input[[paste0("modelChoices_CP", sci)]])){
+ showNotification(paste0("No CP model selected for ",
+ rv$sizeclasses[sci], "...error."), type = "error")
+ return(rv)
+ }
rv$CPmodToUse[sci] <- input[[paste0("modelChoices_CP", sci)]]
if (!grepl("s ~", rv$CPmodToUse[rv$sizeclasses[sci]])){
rv$CPmodToUse[sci] <- paste(rv$CPmodToUse[rv$sizeclasses[sci]], "; NULL", sep = "")
}
rv$CPmodToUse[sci] <- paste("dist: ", rv$CPmodToUse[rv$sizeclasses[sci]], sep = "")
}
+
rv$SEmodToUse <- gsub("~ constant", "~ 1", rv$SEmodToUse)
rv$CPmodToUse <- gsub("~ constant", "~ 1", rv$CPmodToUse)
+
rv$models_SE <- tryCatch(
trimSetSize(rv$mods_SE, rv$SEmodToUse),
error = function(x){NULL}
@@ -945,12 +904,12 @@ update_rv <- function(eventName, rv, input){
rv$M <- NULL
return(rv)
}
-
if (rv$nsizeclasses > 1){
- rv$DWPCol <- NULL
+ dwpcol <- NULL
rv$sizeCol_M <- rv$sizeCol
} else {
rv$DWPCol <- input$DWPCol
+ dwpcol <- rv$DWPCol
rv$sizeCol_M <- NULL
rv$models_SE <- rv$models_SE[[1]]
rv$models_CP <- rv$models_CP[[1]]
@@ -960,11 +919,10 @@ update_rv <- function(eventName, rv, input){
estM(data_CO = rv$data_CO, data_SS = rv$data_SS, data_DWP = rv$data_DWP,
frac = rv$frac, model_SE = rv$models_SE,
model_CP = rv$models_CP,
- COdate = rv$COdate, DWPCol = rv$DWPCol,
+ COdate = rv$COdate, DWPCol = dwpcol,
sizeCol = rv$sizeCol_M, nsim = rv$nsim,
max_intervals = 8
- ), error = function(e) e
- )
+ ), error = function(e) e)
if (!("error" %in% class(rv$M))){
rv$Msplit <- tryCatch(
calcSplits(M = rv$M,
@@ -973,8 +931,18 @@ update_rv <- function(eventName, rv, input){
), error = function(x){NULL}, warning = function(x){NULL}
)
rv$unitCol <- intersect(rv$colNames_CO, rv$colNames_DWP)
- rv$colNames_SS_sel <- colnames(rv$data_SS) %in% rv$data_CO[ ,rv$unitCol]
- rv$colNames_SS_nosel <- rv$colNames_SS[rv$colNames_SS_sel == FALSE]
+ rv$splittable_SS <- rv$colNames_SS
+ badind <- NULL
+ for (ci in 1:length(rv$splittable_SS)){
+ if (is.numeric(rv$data_SS[ , rv$splittable_SS[ci]])){
+ badind <- c(badind, ci)
+ } else {
+ SSlev <- rv$data_SS[ , rv$splittable_SS[ci]]
+ if (min(diff(match(SSlev, unique(SSlev)))) < 0)
+ badind <- c(badind, ci)
+ }
+ }
+ if (length(badind) > 0) rv$splittable_SS <- rv$splittable_SS[-badind]
}
}
@@ -1023,9 +991,19 @@ update_rv <- function(eventName, rv, input){
), error = function(x){NULL}, warning = function(x){NULL}
)
rv$unitCol <- intersect(rv$colNames_CO, rv$colNames_DWP)
- rv$colNames_SS_sel <- colnames(rv$data_SS) %in% rv$data_CO[ ,rv$unitCol]
- rv$colNames_SS_nosel <- rv$colNames_SS[rv$colNames_SS_sel == FALSE]
- }
+ rv$splittable_SS <- rv$colNames_SS
+ badind <- NULL
+ for (ci in 1:length(rv$splittable_SS)){
+ if (is.numeric(rv$data_SS[ , rv$splittable_SS[ci]])){
+ badind <- c(badind, ci)
+ } else {
+ SSlev <- rv$data_SS[ , rv$splittable_SS[ci]]
+ if (min(diff(match(SSlev, unique(SSlev)))) < 0)
+ badind <- c(badind, ci)
+ }
+ }
+ if (length(badind) > 0) rv$splittable_SS <- rv$splittable_SS[-badind]
+ }
}
if (eventName == "transpose_split"){
diff --git a/R/app_utilities.R b/R/app_utilities.R
index d6ad4912a22b0ca4c873b1c35a6d9d99f36f7763..27222e2afef86f6e4b100a379e8e8e575042a156 100644
--- a/R/app_utilities.R
+++ b/R/app_utilities.R
@@ -22,16 +22,13 @@ reVal <- function(rv, toReVal){
}
if("SS" %in% toReVal){
- rv$SS <- seq(0, 364, 7)
+ rv$SS <- NULL #seq(0, 364, 7)
}
if("gSearchInterval " %in% toReVal){
- rv$gSearchInterval <- 7
+ rv$gSearchInterval <- NULL#7
}
if("gSearchMax" %in% toReVal){
- rv$gSearchMax <- 364
- }
- if("SStext" %in% toReVal){
- rv$SStext <- paste(seq(0, 364, 7), collapse = ", ")
+ rv$gSearchMax <- NULL#364
}
if("figH_SE" %in% toReVal){
rv$figH_SE <- 800
@@ -604,10 +601,10 @@ initialReactiveValues <- function(){
colNames_ltp = NULL, colNames_ltp0 = NULL, toRemove_ltp = NULL,
colNames_fta = NULL, colNames_fta0 = NULL, toRemove_fta = NULL,
- colNames_SS = NULL, colNames_SS_sel = NULL, colNames_SS_nosel = NULL,
+ colNames_SS = NULL, splittable_SS = NULL,
colNames_DWP = NULL,
colNames_CO = NULL, colNames_COdates = NULL,
- colNames_size = NULL,
+ colNames_size = NULL, colNames_size0 = NULL,
nsim = 1000, CL = 0.90,
@@ -640,9 +637,8 @@ initialReactiveValues <- function(){
split_CO = NULL, split_SS = NULL, nsplit_CO = 0, nsplit_SS = 0,
figH_M = 600, figW_M = 800,
- SS = seq(0, 364, 7), SStext = paste(seq(0, 364, 7), collapse = ", "),
- avgSI = NULL, SStemp = NULL, gSearchInterval = 7, gSearchMax = 364,
- sizeclasses_g = NULL, nsizeclasses_g = NULL,
+ SS = NULL, avgSI = NULL, SStemp = NULL, gSearchInterval = NULL,
+ gSearchMax = NULL, sizeclasses_g = NULL, nsizeclasses_g = NULL,
gGeneric = NULL, SEmodToUse_g = NULL, CPmodToUse_g = NULL,
figH_g = 400, figW_g = 800,
diff --git a/R/app_widgets.R b/R/app_widgets.R
index d36eb0e182b499f4d3eabee85041a1a0edf9da15..e507a42b1431814e049e941b20a5fde3e78e7742 100644
--- a/R/app_widgets.R
+++ b/R/app_widgets.R
@@ -99,8 +99,8 @@ dataDownloadWidget <- function(set){
#'
#' @param inType Toggle control for the input type of the widget. One of
#' "nsim", "CL", "class", "obsSE", "predsSE", "kFixed", "ltp", "fta",
-#' "predsCP", "dist", "frac", "DWPCol", "COdate", "gSearchInterval",
-#' "gSearchMax", "useSSinputs", or "useSSdata".
+#' "predsCP", "dist", "frac", "DWPCol", "COdate", "gSearchInterval", or
+#' "gSearchMax".
#'
#' @return HTML for the model input widget.
#'
@@ -111,8 +111,7 @@ modelInputWidget <- function(inType){
if (!inType %in% c("nsim", "CL", "class", "obsSE", "predsSE",
"kFixedInput", "ltp", "fta", "predsCP", "dist",
"frac", "DWPCol", "COdate",
- "gSearchInterval", "gSearchMax", "useSSinputs",
- "useSSdata")){
+ "gSearchInterval", "gSearchMax")){
stop(paste0("input inType (", inType, ") not supported"))
}
@@ -132,10 +131,8 @@ modelInputWidget <- function(inType){
"frac" = "Fraction of Facility Surveyed:",
"DWPCol" = "Density Weighted Proportion:",
"COdate" = "Date Found:",
- "gSearchInterval" = "Generic Search Interval (days):",
- "gSearchMax" = "Generic Final Search (day):",
- "useSSinputs" = "Create Custom Generic Schedule",
- "useSSdata" = "Create Average Schedule from SS Data")
+ "gSearchInterval" = "Search Interval (days):",
+ "gSearchMax" = "Total Span of Monitoring (days):")
widgetFun <- switch(inType,
"nsim" = "numericInput",
@@ -152,9 +149,7 @@ modelInputWidget <- function(inType){
"DWPCol" = "selectizeInput",
"COdate" = "selectizeInput",
"gSearchInterval" = "numericInput",
- "gSearchMax" = "numericInput",
- "useSSinputs" = "actionButton",
- "useSSdata" = "actionButton")
+ "gSearchMax" = "numericInput")
Args <- switch(inType,
"nsim" = list(value = 1000, min = 1, max = 10000, step = 1),
@@ -176,10 +171,8 @@ modelInputWidget <- function(inType){
options = list(maxItems = 1)),
"COdate" = list(c("No carcass data input yet"), multiple = TRUE,
options = list(maxItems = 1)),
- "gSearchInterval" = list(value = 7, min = 1, max = 400, step = 1),
- "gSearchMax" = list(value = 364, min = 1, max = 1000, step = 1),
- "useSSinputs" = list(NULL),
- "useSSdata" = list(NULL))
+ "gSearchInterval" = list(value = NULL, min = 1, max = 400, step = 1),
+ "gSearchMax" = list(value = NULL, min = 1, max = 1000, step = 1))
Condition <- switch(inType,
"nsim" = NULL,
@@ -196,9 +189,7 @@ modelInputWidget <- function(inType){
"DWPCol" = "output.DWPNeed == 'yes'",
"COdate" = NULL,
"gSearchInterval" = NULL,
- "gSearchMax" = NULL,
- "useSSinputs" = NULL,
- "useSSdata" = "output.data_SS != null")
+ "gSearchMax" = NULL)
widgetMaker(Condition, Name, widgetFun, Label, Args)
}
@@ -281,13 +272,15 @@ modelRunWidget <- function(modType){
"M" = "input.modelChoices_SE1 != null &
input.modelChoices_CP1 != null &
output.sizeclasses_SE == output.sizeclasses_CP &
- output.data_SS != null & output.kNeed != 'yes' &
- input.DWPCol != null & input.COdate != null",
+ output.filename_SS != null & output.kNeed != 'yes' &
+ input.DWPCol != null &
+ input.COdate != null",
"g" = "input.modelChoices_SE1 != null &
input.modelChoices_CP1 != null &
output.kNeed != 'yes' &
output.sizeclasses_SE == output.sizeclasses_CP")
+
cName <- switch(modType,
"SE" = "run_SE_clear",
"CP" = "run_CP_clear",
@@ -342,7 +335,7 @@ preTextMaker <- function(modType){
"M" = c("input.modelChoices_SE1 == null |
input.modelChoices_CP1 == null |
output.sizeclasses_SE != output.sizeclasses_CP",
- "output.data_SS == null",
+ "output.filename_SS == null",
"input.modelChoices_SE1 != null &
input.modelChoices_CP1 != null &
output.sizeclasses_SE == output.sizeclasses_CP &
diff --git a/R/carcass_persistence_functions.R b/R/carcass_persistence_functions.R
index ecdbb6ed34825c0105a66d9a7aa5de4f840de2d4..419f923587d4b6a7143ef51fd1a45deca19cf6e4 100644
--- a/R/carcass_persistence_functions.R
+++ b/R/carcass_persistence_functions.R
@@ -219,6 +219,8 @@ cpm0 <- function(formula_l, formula_s = NULL, data = NULL, left = NULL,
right = NULL, dist = "weibull", CL = 0.90, quiet = FALSE){
dist <- tolower(dist)
# initial error-checking
+ ind <- sapply(data, is.factor)
+ data[ind] <- lapply(data[ind], as.character)
if (length(left) == 0){
left <- "left"
if (!"left" %in% colnames(data)){
diff --git a/R/detection_probability_figure_functions.R b/R/detection_probability_figure_functions.R
index 6cf1f7b1e151e28f3e14558ccb9f4d66b165a25d..0b66396809f1b128ca6d28158754e5bf8900391d 100644
--- a/R/detection_probability_figure_functions.R
+++ b/R/detection_probability_figure_functions.R
@@ -41,7 +41,6 @@ plot.gGeneric <- function(x, CL = 0.90, ...){
)
axis(2, at = seq(0, 1, 0.2), las = 1, cex.axis = 1)
mtext(side = 2, line = 2.75, "Detection Probability", cex = 1.25)
-
axis(1, at = 1:ncell, labels = FALSE)
ang <- 0
offx <- NULL
diff --git a/R/detection_probability_functions.R b/R/detection_probability_functions.R
index 9a29f9b927e82a498b0160f25d903432d9932368..8eec602e91389be8875e85f00e8d895443fb57c2 100644
--- a/R/detection_probability_functions.R
+++ b/R/detection_probability_functions.R
@@ -62,6 +62,10 @@ estg <- function(data_CO, COdate, data_SS, SSdate = NULL,
model_SE, model_CP, sizeCol = NULL, unitCol = NULL,
nsim = 1000, max_intervals = 8,
seed_SE = NULL, seed_CP = NULL, seed_g = NULL){
+ i <- sapply(data_CO, is.factor)
+ data_CO[i] <- lapply(data_CO[i], as.character)
+ i <- sapply(data_SS, is.factor)
+ data_SS[i] <- lapply(data_SS[i], as.character)
# error-checking
if (is.null(unitCol))
@@ -126,6 +130,10 @@ estg <- function(data_CO, COdate, data_SS, SSdate = NULL,
"' Aborting calculation of ghat."
)
}
+ if (any(unlist(lapply(model_SE, function(x) x$pOnly)))){
+ nok <- names(which(unlist(lapply(model_SE, function(x) x$pOnly))))
+ stop("valid k not provided for ", paste(nok, collapse = " "))
+ }
}
preds_SE <- lapply(model_SE, function(x) x$predictors)
@@ -495,7 +503,9 @@ estgGeneric <- function(days, model_SE, model_CP, nsim = 1000, seed_SE = NULL,
ghat[[celli]] <- calcg(days, param_SE, param_CP, dist)
}
names(ghat) <- preds$CellNames
- out <- list("ghat" = ghat, "predictors" = preds)
+ span <- max(days)
+ I <- span/(length(days) - 1)
+ out <- list("ghat" = ghat, "predictors" = preds, "SS" = list(span = span, I = I))
class(out) <- c("gGeneric", "list")
return(out)
}
@@ -548,6 +558,13 @@ calcg <- function(days, param_SE, param_CP, dist){
f0 <- mean(pk[, 1])
k0 <- mean(pk[, 2])
+ if (length(days) == 2){ # then only one search, so it's easy!
+ r_sim <- as.vector(ppersist(dist = dist,
+ t_arrive0 = days[1], t_arrive1 = days[2], t_search = days[2],
+ pda = param_CP[,1], pdb = param_CP[,2]))
+ return(r_sim * pk[ , 1])
+ }
+
###1. setting estimation control parameters
ind1 <- rep(1:nsearch, times = nsearch:1)
ind2 <- ind1 + 1
diff --git a/R/model_utility_functions.R b/R/model_utility_functions.R
index f04636b56391e799444f3bd6a37381996585f720..018a27c8d37577a324d25a44da04be23eaf7d636 100644
--- a/R/model_utility_functions.R
+++ b/R/model_utility_functions.R
@@ -12,6 +12,8 @@
#' @export
#'
combinePreds <- function(preds, data){
+ ind <- sapply(data, is.factor)
+ data[ind] <- lapply(data[ind], as.character)
npred <- length(preds)
if (npred == 0){
@@ -176,6 +178,10 @@ matchCells <- function(specific, modelSet){
#' @export
#'
combinePredsAcrossModels <- function(preds_CP, preds_SE, data_CP, data_SE){
+ ind <- sapply(data_CP, is.factor)
+ data_CP[ind] <- lapply(data_CP[ind], as.character)
+ ind <- sapply(data_SE, is.factor)
+ data_SE[ind] <- lapply(data_SE[ind], as.character)
preds <- unique(c(preds_SE, preds_CP))
npred <- length(preds)
diff --git a/R/mortality_functions.R b/R/mortality_functions.R
index bf198d0789ca2e1948edae60fd17f8185bbcdbf1..a983ecbea2f10dcd04c1d9ab2aab2e71515ccc66 100644
--- a/R/mortality_functions.R
+++ b/R/mortality_functions.R
@@ -75,6 +75,12 @@ estM <- function(data_CO, data_SS, data_DWP, frac = 1,
DWPCol = NULL, seed_SE = NULL, seed_CP = NULL, seed_g = NULL,
seed_M = NULL, nsim = 1000, max_intervals = 8){
+ i <- sapply(data_CO, is.factor)
+ data_CO[i] <- lapply(data_CO[i], as.character)
+ i <- sapply(data_SS, is.factor)
+ data_SS[i] <- lapply(data_SS[i], as.character)
+ i <- sapply(data_DWP, is.factor)
+ data_DWP[i] <- lapply(data_DWP[i], as.character)
if (!(COdate %in% colnames(data_CO))){
stop("COdate not found in data_CO")
diff --git a/R/searcher_efficiency_functions.R b/R/searcher_efficiency_functions.R
index 576469e0bcaa2afea9b28f8edbe21dfdadc48235..5cd121384aa6edade55cfd77b24a99ddd438ce86 100644
--- a/R/searcher_efficiency_functions.R
+++ b/R/searcher_efficiency_functions.R
@@ -200,7 +200,12 @@
pkm <- function(formula_p, formula_k = NULL, data, obsCol = NULL, kFixed = NULL,
allCombos = FALSE, sizeCol = NULL,
CL = 0.90, kInit = 0.7, quiet = FALSE){
-
+ if (!is.null(kFixed) && !is.numeric(kFixed))
+ stop("kFixed must be NULL or numeric")
+ if (any(na.omit(kFixed) < 0 | na.omit(kFixed) > 1)){
+ badk <- names(which(na.omit(kFixed) < 0 | na.omit(kFixed) > 1))
+ stop("invalid k for ", paste0(badk, collapse = ", "))
+ }
if (is.null(allCombos) || is.na(allCombos) || !is.logical(allCombos)){
stop("allCombos must be TRUE or FALSE")
}
@@ -224,8 +229,14 @@ pkm <- function(formula_p, formula_k = NULL, data, obsCol = NULL, kFixed = NULL,
#' @export
pkm0 <- function(formula_p, formula_k = NULL, data, obsCol = NULL,
kFixed = NULL, kInit = 0.7, CL = 0.90, quiet = FALSE, ...){
-
+ i <- sapply(data, is.factor)
+ data[i] <- lapply(data[i], as.character)
if (!is.null(kFixed) && is.na(kFixed)) kFixed <- NULL
+ if (!is.null(kFixed) && !is.numeric(kFixed))
+ stop("kFixed must be NULL or numeric")
+ if (!is.null(kFixed) && (kFixed < 0 | kFixed > 1)){
+ stop("invalid fixed value for k")
+ }
if(any(! obsCol %in% colnames(data))){
stop("Observation column provided not in data.")
}
diff --git a/R/splits_functions.R b/R/splits_functions.R
index 7f4dd9cb717b51bb0f86a1ac575a8647b70f0de4..25bff7cf7b9e54fc0c8e4b52226e9466d80bec80 100644
--- a/R/splits_functions.R
+++ b/R/splits_functions.R
@@ -217,6 +217,11 @@ calcTsplit <- function(rate, days, tsplit){
calcSplits <- function(M, split_CO = NULL, data_CO = NULL,
split_SS = NULL, data_SS = NULL, split_time = NULL,
...){
+ ind <- sapply(data_CO, is.factor)
+ data_CO[ind] <- lapply(data_CO[ind], as.character)
+ ind <- sapply(data_SS, is.factor)
+ data_SS[ind] <- lapply(data_SS[ind], as.character)
+
##### read data and check for errors
if ("list" %in% class(M)){
if (!all(c("Mhat", "Aj") %in% names(M))){
@@ -346,6 +351,8 @@ calcSplits <- function(M, split_CO = NULL, data_CO = NULL,
stop("split_SS column must be categorical or dates")
}
SSlevel <- data_SS[[split_SS]]
+ if (length(unique(SSlevel)) == 1)
+ stop("split_SS = ", split_SS, "has only one level...nothing to split on")
if (min(diff(match(SSlevel, unique(SSlevel)))) < 0){
stop(
"split_SS levels must be in contiguous blocks in data_SS.\n",
@@ -380,7 +387,7 @@ calcSplits <- function(M, split_CO = NULL, data_CO = NULL,
split_v <- list()
split_v[["name"]] <- ifelse(!is.null(split_h), split_CO[1], split_CO[2])
split_v[["vals"]] <- data_CO[[split_v$name]]
- split_v[["level"]] <- unique(split_v$vals)
+ split_v[["level"]] <- gtools::mixedsort(unique(split_v$vals))
split_v[["nlev"]] <- length(split_v$level)
split_v[["type"]] <- "CO"
}
@@ -388,7 +395,7 @@ calcSplits <- function(M, split_CO = NULL, data_CO = NULL,
split_h <- list()
split_h[["name"]] <- split_CO[1]
split_h[["vals"]] <- data_CO[[split_h$name]]
- split_h[["level"]] <- unique(split_h$vals)
+ split_h[["level"]] <- gtools::mixedsort(unique(split_h$vals))
split_h[["nlev"]] <- length(split_h$level)
split_h[["type"]] <- "CO"
}
@@ -425,6 +432,7 @@ calcSplits <- function(M, split_CO = NULL, data_CO = NULL,
}
splits[["X"]][li] <- length(lind)
}
+
} else if (split_h$type %in% c("time", "SS")){
days <- data_SS$days
rate <- calcRate(M, Aj, days = days,
@@ -558,7 +566,7 @@ summary.splitFull <- function(object, CL = 0.90, ...){
"At most two split variables are allowed."
)
}
- # order the non-temporal dimensions "alphabetically"
+ # order the non-temporal dimensions "alphabetically" (done in calcSplits?)
if (!is.null(attr(splits, "type"))){
if (!is.list(splits$M)){
if (attr(splits, "type") == "CO"){
@@ -613,6 +621,9 @@ ltranspose <- function(M){
ans <- list()
for (i in 1:adim[1]){
ans[[i]] <- do.call("rbind", lapply(M, function(x) x[i, ]))
+# if (attributes(M)$type)[1] == "CO"){
+# ans[[i]] <- ans[[i]][order(names(ans[[i]]),]
+# }
}
return(ans)
}
diff --git a/R/utility_functions.R b/R/utility_functions.R
index 3a5ffe849cd7bb95888052bcb1b30f7fccd34adc..b8f9e446a3a7c5eebf1776c0debf02e4a6e96e45 100644
--- a/R/utility_functions.R
+++ b/R/utility_functions.R
@@ -226,6 +226,13 @@ print.corpus_frame <- function(x, ...){
#' @export
defineUnitCol <- function(data_CO, data_SS = NULL, data_DWP = NULL){
+ ind <- sapply(data_SS, is.factor)
+ data_SS[ind] <- lapply(data_SS[ind], as.character)
+ ind <- sapply(data_CO, is.factor)
+ data_CO[ind] <- lapply(data_CO[ind], as.character)
+ ind <- sapply(data_DWP, is.factor)
+ data_DWP[ind] <- lapply(data_DWP[ind], as.character)
+
if (is.null(data_CO)) stop("data_CO empty")
if (is.null(data_SS) && is.null(data_DWP))
stop("cannot find an unambiguous unit column in data_CO")
@@ -237,7 +244,7 @@ defineUnitCol <- function(data_CO, data_SS = NULL, data_DWP = NULL){
"unit column. Cannot estimate M"
)
}
- if (length(unitCol) == 1 & any(!(data_CO[ , unitCol] %in% data_DWP[ , unitCol]))){
+ if (length(unitCol) == 1 && any(!(data_CO[ , unitCol] %in% data_DWP[ , unitCol]))){
ind <- which(!(data_CO[ , unitCol] %in% data_DWP[ , unitCol]))
if (length(ind) > 1)
stop("Units ", paste0(data_CO[ind, unitCol], collapse = ", "), " are ",
@@ -246,8 +253,26 @@ defineUnitCol <- function(data_CO, data_SS = NULL, data_DWP = NULL){
"represented in data_CO but not in data_DWP. Cannot estimate M.")
}
if (length(unitCol) > 1){
- stop("data_CO and data_DWP have columns ", paste0(unitCol, collapse = ", "),
- " in common. Cannot unambiguously define a unit column")
+ badind <- NULL
+ for (ui in 1:length(unitCol)){
+ if (length(data_DWP[ , ui]) != length(unique(data_DWP[, ui]))){
+ badind <- c(badind, ui)
+ next
+ }
+ if (any(! data_CO[, ui] %in% data_DWP[ ,ui])){
+ badind <- c(badind, ui)
+ next
+ }
+ }
+ if (length(badind) > 0){
+ unitCol <- unitCol[-badind]
+ }
+ if (length(unitCol) == 0){
+ stop("No shared column in DWP and CO files meets the criteria for ",
+ "a unit column: either no common column name, or some CO ",
+ "'units' not represented in corresponding DWP 'units', or ",
+ "'units' listed in DWP are not unique.")
+ }
}
return(unitCol)
}
diff --git a/REVISIONS.txt b/REVISIONS.txt
index d19b6944db12cab927fb150f0e4ab9c81df5317a..1bd4c23400ace62fa3641619cf479eb9cd63df50 100644
--- a/REVISIONS.txt
+++ b/REVISIONS.txt
@@ -1,5 +1,58 @@
Revision log
+2018.12.09
+v1.2.1
+
+* changed the conditional run_M and run_g buttons to use output.filename_SS
+rather than output.data_SS as a check. The check on output.data_SS required
+users who uploaded example data into the GUI to click on the "Search Schedule"
+panel before the run_M and run_g buttons could be activated. This change
+relieves an inconvenience that GUI users would have when analyzing the example
+data sets (GUI).
+
+* corrected a check on data structure that was preventing estimation of
+mortality in some very simple data sets, namely, data with no carcass size
+distinctions, only one search occasion represented in the SE data, and using
+a fixed k parameter (GUI).
+
+* fixed a bug that was preventing mortality estimation in cases where there
+was more than one size class and more than one estimate of mortality was to
+be made in succession (GUI).
+
+* streamlined the interface under the "Detection Probability" tab by removing
+the requirement that, after entering new search schedule parameters, users must
+calculate the new search schedule derived from the parameters before clicking
+"Estimate" to get the new g's based on the new search schedule. Also, added the
+search schedule to the results table's caption to make it clear which search
+parameters the g_hat's are associated with (GUI).
+
+* the default search schedule that is entered into the numericInput boxes under
+the "Detection Probability" calculation tab is now NULL when no data_SS is
+loaded and averageSS when a data_SS is loaded. The new defaults are a better
+representation (averageSS) of the uploaded data than are the previous
+one-size-fits-all parameters of I = 7, span = 364. However, the "Detection
+Probability" tab SS parameters are not used in mortality estimation and are
+strictly a user convenience for data exploration and can be readily changed
+with no downstream consequences on results outside the specialized "Detection
+Probability" page (GUI).
+
+* two-dimensional transposeSplits was assigning some splits estimates to the
+wrong splits levels because of an array ordering issue. This has been corrected
+by initial ordering of CO splits in calcSplits. (command line and GUI)
+
+* added additional error-checks for failure to select CP and SE models for all
+size classes. Previously, if models were selected for the first size class but
+no others, the "Estimate" buttons were activated for both M and genericg, but
+clicking them would crash GenEst (gray screen). Now, the buttons are still
+activated, but a new error-check catches the problem and displays a useful
+error message in lieu of crashing. (GUI)
+
+* added code to convert dataframe "factors" to "characters" prior to performing
+calculations in pkm0, cpm0, estM, estg,
+
+* figure downloaded for estimated mortality corrected to properly reflect user's
+custom-selected CL
+
2018.12.06
v1.2.0
diff --git a/code.json b/code.json
index cd5327ef95a36448de573b08d14abdc33e1ba107..d985bc5e3e23bfc8393975eff129ce54789d9e20 100644
--- a/code.json
+++ b/code.json
@@ -3,7 +3,7 @@
"Name": "GenEst",
"organization": "U.S. Geological Survey",
"description": "R package for estimating bird and bat fatalities at wind and solar power facilities",
- "version": "1.2.0",
+ "version": "1.2.1",
"status": "Release Candidate",
"permissions": {
@@ -44,7 +44,7 @@
},
"date": {
- "metadataLastUpdated": "2018-12-06"
+ "metadataLastUpdated": "2018-12-18"
}
} }
}
diff --git a/inst/doc/GenEstGUI.R b/inst/doc/GenEstGUI.R
deleted file mode 100644
index 8b137891791fe96927ad78e64b0aad7bded08bdc..0000000000000000000000000000000000000000
--- a/inst/doc/GenEstGUI.R
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/inst/doc/GenEstGUI.Rmd b/inst/doc/GenEstGUI.Rmd
deleted file mode 100644
index 2a4eeeaca304a2df2a50c964e8b3279315254ecb..0000000000000000000000000000000000000000
--- a/inst/doc/GenEstGUI.Rmd
+++ /dev/null
@@ -1,287 +0,0 @@
----
-title: "GenEst - Graphic User Interface"
-author: "Juniper L. Simonis"
-date: "`r Sys.Date()`"
-output: rmarkdown::html_vignette
-vignette: >
- %\VignetteIndexEntry{GenEst - 4. Graphic User Interface}
- %\VignetteEngine{knitr::rmarkdown}
- \usepackage[utf8]{inputenc}
----
-
-# Overview
-
-This document describes the codebase used to create the GenEst Graphic User
-Interface (GUI). The Genest GUI is coded in HTML via external R packages
-([DT](https://cran.r-project.org/package=DT),
-[htmltools](https://cran.r-project.org/package=htmltools),
-[shiny](https://cran.r-project.org/package=shiny),
-[shinyjs](https://cran.r-project.org/package=shinyjs),
-as well as a number of internal functions to facilitate a simple,
-human-readable codebase underlying the app. The goal being to allow GenEst
-to evolve as fluidly as possible at the user interface.
-
-# Execution
-
-The GUI is executed locally or as a deployed app following the basic approach
-of shiny applications. For ease of implementation, we have created an overall
-function to intialize the app, `runGenEst()`, which calls both the server and
-UI codebases. Like most vintage Shiny apps, we employee the two-file system,
-including a `ui.R` and `server.R` script, although each script is Spartan. The
-`ui.R` script includes a single call to the `GenEstUI(appType)` function,
-which starts the cascade of HTML-generating functions outlined in
-**UI Function Hierarchy**. The `server.R` script includes a single reference
-(not a call) to the function `GenEstServer`, which is detailed in
-**Server Function Hierarchy**.
-
-# User Interface
-
-### UI Function Hierarchy
-
-The GenEst User Interface is constructed in HTML using pages, panels,
-tabs, and widgets. The code is parsed into a series of hierarchical functions
-to facilitate readability and mobility of specific UI components.
-
-* `GenEstUI(appType)`
- * `dataInputPanel()`
- * `dataInputSidebar()`
- * `dataInputWidget("SE")`
- * `dataInputWidget("CP")`
- * `dataInputWidget("SS")`
- * `dataInputWidget("DWP")`
- * `dataInputWidget("CO")`
- * `loadedDataPanel()`
- * `dataTabPanel("SE")`
- * `dataTabPanel("CP")`
- * `dataTabPanel("SS")`
- * `dataTabPanel("DWP")`
- * `dataTabPanel("CO")`
- * `analysisPanel()`
- * `GeneralInputsPanel()`
- * `GeneralInputsSidebar()`
- * `modelInputWidget("nsim")`
- * `modelInputWidget("CL")`
- * `modelInputWidget("sizeclassCol")`
- * `SEPanel()`
- * `SESidebar()`
- * `modelInputWidget("obsSE")`
- * `modelInputWidget("predsSE")`
- * `modelInputWidget("kFixed")`
- * `modelRunWidget("SE")`
- * `modelOutputWidget("SE")`
- * `SEMainPanel()`
- * `selectedDataPanel("SE")`
- * `modelOutputPanel("SEFigures")`
- * `modelOutputPanel("SEEstimates")`
- * `modelOutputPanel("SEModComparison")`
- * `modelOutputPanel("SEModSelection")`
- * `CPPanel()`
- * `CPSidebar()`
- * `modelInputWidget("ltp")`
- * `modelInputWidget("fta")`
- * `modelInputWidget("predsCP")`
- * `modelInputWidget("dists")`
- * `modelRunWidget("CP")`
- * `modelOutputWidget("CP")`
- * `CPMainPanel()`
- * `selectedDataPanel("CP")`
- * `modelOutputPanel("CPFigures")`
- * `modelOutputPanel("CPEstimates")`
- * `modelOutputPanel("CPModComparison")`
- * `modelOutputPanel("CPModSelection")`
- * `MPanel()`
- * `MSidebar()`
- * `modelInputWidget("frac")`
- * `modelInputWidget("DWPCol")`
- * `modelInputWidget("dateFoundCol")`
- * `modelRunWidget("M")`
- * `modelOutputWidget("M")`
- * `MMainPanel()`
- * `gPanel()`
- * `gSidebar()`
- * `modelInputWidget("gSearchInterval")`
- * `modelInputWidget("gSearchMax")`
- * `modelInputWidget("useSSinputs")`
- * `modelInputWidget("useSSdata")`
- * `modelRunWidget("g")`
- * `modelOutputWidget("g")`
- * `gMainPanel()`
- * `selectedDataPanel("g")`
- * `modelOutputPanel("gFigures")`
- * `modelOutputPanel("gSummary")`
- * `helpPanel(type)`
- * `gettingStartedPanel()`
- * `gettingStartedContent()`
- * `downloadsPanel()`
- * `dataDownloadsWidget("RP")`
- * `dataDownloadsWidget("RPbat")`
- * `dataDownloadsWidget("cleared")`
- * `dataDownloadsWidget("powerTower")`
- * `dataDownloadsWidget("PV")`
- * `dataDownloadsWidget("trough")`
- * `dataDownloadsWidget("mock")`
- * `aboutPanel()`
- * `aboutContent()`
- * `GenEstAuthors()`
- * `GenEstGUIauthors()`
- * `GenEstLicense()`
- * `GenEstAcknowledgements()`
- * `GenEstLogos()`
- * `disclaimersPanel(appType)`
- * `disclaimerContent(appType)`
- * `disclaimerUSGS()`
- * `disclaimerWEST(appType)`
-
-### UI Widgets
-
-We have coded up a number of widget functions, some of which are simple
-wrappers on shiny functions that help reduce code clutter, and others of which
-are custom HTML (e.g., for model selection), but which are still nonetheless
-wrapped over shiny widgets:
-
-* `dataInputWidget(dataType)`
-* `modelInputWidget(inType)`
-* `modelRunWidget(modType)`
-* `modelOutputWidget(modType)`
-* `dataDownloadsWidget(set)`
-* `modelSelectionWidget(modType)`
-* `kFixedWidget()`
-
-A major need for widgets is having a simple condition wrapped on it, such that
-the widget is within a conditional panel, defined by some other input or
-output variable. To facilitate coding of these widgets, we have made a function
-`widgetMaker`, which is a generalized constructor function.
-
-### UI Panels
-
-Similarly to the input widgets, we have coded up a number of output panel
-functions that help direct traffic in the building on the HTML document. These
-functions are generalized to the suite of possible options for panels currently
-needed by leveraging the basic template approach with limited variation.
-
-* `dataTabPanel(dataType)`
-* `selectedDataPanel(modType)`
-* `modelOutputPanel(outType)`
-
-### UI Content
-
-There is a fair amount of (mostly) static content, which we have contained
-wihtin functions to reduce overall code clutter. These functions primarily
-dictate content within the "Help" tab's subtabs.
-
-* `gettingStartedContent()`
-* `aboutContent()`
-* `GenEstAuthors()`
-* `GenEstGUIAuthors()`
-* `GenEstLicense()`
-* `GenEstAcknowledgements()`
-* `GenEstLogos()`
-* `disclaimersContent(appType)`
-* `disclaimerUSGS()`
-* `disclaimerWEST(appType)`
-
-# Server Functionality
-
-### Reactivity
-
-The server-side functionality operates using [Shiny's reactive
-programming](https://shiny.rstudio.com/articles/reactivity-overview.html)
-framework. In particular, we include a main `reactiveValues` list called
-`rv`, that, in addition to the standard `input` and `output` lists is
-passed among functions throughout the app. The `rv` list holds all of the
-reactive values currently being used by the application.
-
-### Server Function Hierarchy
-
-The GenEst server code is a relatively flat hierarchy, especially in
-comparison to the UI code. In particular, after a brief set of preamble
-functions for preparing objects and options, `GenEstServer` makes many calls
-to `observeEvent`, the reactive observation function, one call for each
-of the possible events in the application (data load, model run or clear,
-column selection, clear of contents). Each call also includes an
-evaluation of the held-back "handler" code for the event returned by
-the function `reaction`, with the handler being the code that
-is to be evaluated when the event is observed. The expression is held-back
-in `reaction` to minimize scoping issues associated with message-related
-functions.
-
-* `GenEstServer(input, output, session)`
- * `rv <- initialReactiveValues()`
- * `output <- initialOutput(rv, output)`
- * `msgs <- msgList()`
- * `options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))`
- * `observeEvent(DT.options = list(pageLength = 25))`
- * `observeEvent(input$clear_all, eval(reaction("clear_all")))`
- * `observeEvent(input$file_SE, eval(reaction("file_SE")))`
- * `observeEvent(input$file_SE_clear, eval(reaction("file_SE_clear")))`
- * `observeEvent(input$file_CP, eval(reaction("file_CP")))`
- * `observeEvent(input$file_CP_clear, eval(reaction("file_CP_clear")))`
- * `observeEvent(input$file_SS, eval(reaction("file_SS")))`
- * `observeEvent(input$file_SS_clear, eval(reaction("file_SS_clear")))`
- * `observeEvent(input$file_DWP, eval(reaction("file_DWP")))`
- * `observeEvent(input$file_DWP_clear, eval(reaction("file_DWP_clear")))`
- * `observeEvent(input$file_CO, eval(reaction("file_CO")))`
- * `observeEvent(input$file_CO_clear, eval(reaction("file_CO_clear")))`
-
- * `observeEvent(input$class, eval(reaction("class")), ignoreNULL = FALSE)`
-
- * `observeEvent(input$obsSE, eval(reaction("obsSE")), ignoreNULL = FALSE)`
- * `observeEvent(input$predsSE, eval(reaction("predsSE")), ignoreNULL = FALSE)`
- * `observeEvent(input$run_SE, eval(reaction("run_SE")))`
- * `observeEvent(input$run_SE_clear, eval(reaction("run_SE_clear")))`
- * `observeEvent(input$outSEclass, eval(reaction("outSEclass")))`
- * `observeEvent(input$outSEp, eval(reaction("outSEp")))`
- * `observeEvent(input$outSEk, eval(reaction("outSEk")))`
-
- * `observeEvent(input$ltp, eval(reaction("ltp")), ignoreNULL = FALSE)`
- * `observeEvent(input$fta, eval(reaction("fta")), ignoreNULL = FALSE)`
- * `observeEvent(input$predsCP, eval(reaction("predsCP")), ignoreNULL = FALSE)`
- * `observeEvent(input$run_CP, eval(reaction("run_CP")))`
- * `observeEvent(input$run_CP_clear, eval(reaction("run_CP_clear")))`
- * `observeEvent(input$outCPclass, eval(reaction("outCPclass")))`
- * `observeEvent(input$outCPdist, eval(reaction("outCPdist")))`
- * `observeEvent(input$outCPl, eval(reaction("outCPl")))`
- * `observeEvent(input$outCPs, eval(reaction("outCPs")))`
-
- * `observeEvent(input$run_M, eval(reaction("run_M")))`
- * `observeEvent(input$run_M_clear, eval(reaction("run_M_clear")))`
- * `observeEvent(input$split_M, eval(reaction("split_M")))`
- * `observeEvent(input$split_M_clear, eval(reaction("split_M_clear")))`
- * `observeEvent(input$transpose_split, eval(reaction("transpose_split")))`
-
- * `observeEvent(input$useSSdata, eval(reaction("useSSdata")))`
- * `observeEvent(input$useSSinputs, eval(reaction("useSSinputs")))`
- * `observeEvent(input$run_g, eval(reaction("run_g")))`
- * `observeEvent(input$run_g_clear, eval(reaction("run_g_clear")))`
- * `observeEvent(input$outgclass, eval(reaction("outgclass")))`
-
-The `reaction` function is, essentially, a parsed-text-generating
-function. Depending upon the specific event (`eventName`, the only input), the
-necessary set of function calls (messages for running, running the code,
-messages for when done) is prepared for evaluation. The main function used to
-do things within the handler code (when the event occurs) is `eventReaction`,
-which calls three functions: `update_rv`, `update_output`, and `update_input`,
-in that order (updating the output depends on the rv being updated already
-and updating the input requires both the rv and output to be up-to-date):
-
-* `eventReaction(eventName, rv, input, output, session)`
- * `update_rv(eventName, rv, input)`
- * `update_output(eventName, rv, output)`
- * `update_input(eventName, rv, input, session)`
-
-Because of the scoping set-up for Shiny apps, there is no need to assign
-the returned elements for `update_rv` and `update_output` to anything, and
-`update_input` works through `session` to direct its updates to the
-application.
-
-Each of the three functions takes `eventName` as the first argument,
-which is used to toggle amongst the possible actions to be taken with
-respect to each of the lists. That is, each of the three `update_` functions
-contains a large internal set of routines, and only the relevant ones are
-called for a given function. This occurs via simple conditional code blocks
-("if the eventType is this, do this") for each of the possible events,
-thereby reducing the number of specific functions, but increasing the size
-of the key functions. Within each of the three functions, some of the events
-trigger a substantial amount of code while others only trigger a few (or no)
-lines. Similarly, some of the handler expressions take virutally no time to
-run, while others take a few minutes.
\ No newline at end of file
diff --git a/inst/doc/GenEstGUI.html b/inst/doc/GenEstGUI.html
deleted file mode 100644
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-<h1 class="title toc-ignore">GenEst - Graphic User Interface</h1>
-<h4 class="author"><em>Juniper L. Simonis</em></h4>
-<h4 class="date"><em>2018-12-05</em></h4>
-
-
-
-<div id="overview" class="section level1">
-<h1>Overview</h1>
-<p>This document describes the codebase used to create the GenEst Graphic User Interface (GUI). The Genest GUI is coded in HTML via external R packages (<a href="https://cran.r-project.org/package=DT">DT</a>, <a href="https://cran.r-project.org/package=htmltools">htmltools</a>, <a href="https://cran.r-project.org/package=shiny">shiny</a>, <a href="https://cran.r-project.org/package=shinyjs">shinyjs</a>, as well as a number of internal functions to facilitate a simple, human-readable codebase underlying the app. The goal being to allow GenEst to evolve as fluidly as possible at the user interface.</p>
-</div>
-<div id="execution" class="section level1">
-<h1>Execution</h1>
-<p>The GUI is executed locally or as a deployed app following the basic approach of shiny applications. For ease of implementation, we have created an overall function to intialize the app, <code>runGenEst()</code>, which calls both the server and UI codebases. Like most vintage Shiny apps, we employee the two-file system, including a <code>ui.R</code> and <code>server.R</code> script, although each script is Spartan. The <code>ui.R</code> script includes a single call to the <code>GenEstUI(appType)</code> function, which starts the cascade of HTML-generating functions outlined in <strong>UI Function Hierarchy</strong>. The <code>server.R</code> script includes a single reference (not a call) to the function <code>GenEstServer</code>, which is detailed in <strong>Server Function Hierarchy</strong>.</p>
-</div>
-<div id="user-interface" class="section level1">
-<h1>User Interface</h1>
-<div id="ui-function-hierarchy" class="section level3">
-<h3>UI Function Hierarchy</h3>
-<p>The GenEst User Interface is constructed in HTML using pages, panels, tabs, and widgets. The code is parsed into a series of hierarchical functions to facilitate readability and mobility of specific UI components.</p>
-<ul>
-<li><code>GenEstUI(appType)</code>
-<ul>
-<li><code>dataInputPanel()</code>
-<ul>
-<li><code>dataInputSidebar()</code>
-<ul>
-<li><code>dataInputWidget("SE")</code></li>
-<li><code>dataInputWidget("CP")</code></li>
-<li><code>dataInputWidget("SS")</code></li>
-<li><code>dataInputWidget("DWP")</code></li>
-<li><code>dataInputWidget("CO")</code></li>
-</ul></li>
-<li><code>loadedDataPanel()</code>
-<ul>
-<li><code>dataTabPanel("SE")</code></li>
-<li><code>dataTabPanel("CP")</code></li>
-<li><code>dataTabPanel("SS")</code></li>
-<li><code>dataTabPanel("DWP")</code></li>
-<li><code>dataTabPanel("CO")</code></li>
-</ul></li>
-<li><code>analysisPanel()</code></li>
-<li><code>GeneralInputsPanel()</code>
-<ul>
-<li><code>GeneralInputsSidebar()</code>
-<ul>
-<li><code>modelInputWidget("nsim")</code></li>
-<li><code>modelInputWidget("CL")</code></li>
-<li><code>modelInputWidget("sizeclassCol")</code><br />
-</li>
-</ul></li>
-</ul></li>
-<li><code>SEPanel()</code>
-<ul>
-<li><code>SESidebar()</code>
-<ul>
-<li><code>modelInputWidget("obsSE")</code></li>
-<li><code>modelInputWidget("predsSE")</code></li>
-<li><code>modelInputWidget("kFixed")</code></li>
-<li><code>modelRunWidget("SE")</code></li>
-<li><code>modelOutputWidget("SE")</code><br />
-</li>
-</ul></li>
-<li><code>SEMainPanel()</code>
-<ul>
-<li><code>selectedDataPanel("SE")</code></li>
-<li><code>modelOutputPanel("SEFigures")</code></li>
-<li><code>modelOutputPanel("SEEstimates")</code></li>
-<li><code>modelOutputPanel("SEModComparison")</code></li>
-<li><code>modelOutputPanel("SEModSelection")</code><br />
-</li>
-</ul></li>
-</ul></li>
-<li><code>CPPanel()</code>
-<ul>
-<li><code>CPSidebar()</code>
-<ul>
-<li><code>modelInputWidget("ltp")</code></li>
-<li><code>modelInputWidget("fta")</code></li>
-<li><code>modelInputWidget("predsCP")</code></li>
-<li><code>modelInputWidget("dists")</code></li>
-<li><code>modelRunWidget("CP")</code></li>
-<li><code>modelOutputWidget("CP")</code></li>
-</ul></li>
-<li><code>CPMainPanel()</code>
-<ul>
-<li><code>selectedDataPanel("CP")</code></li>
-<li><code>modelOutputPanel("CPFigures")</code></li>
-<li><code>modelOutputPanel("CPEstimates")</code></li>
-<li><code>modelOutputPanel("CPModComparison")</code></li>
-<li><code>modelOutputPanel("CPModSelection")</code><br />
-</li>
-</ul></li>
-</ul></li>
-<li><code>MPanel()</code>
-<ul>
-<li><code>MSidebar()</code>
-<ul>
-<li><code>modelInputWidget("frac")</code></li>
-<li><code>modelInputWidget("DWPCol")</code></li>
-<li><code>modelInputWidget("dateFoundCol")</code></li>
-<li><code>modelRunWidget("M")</code></li>
-<li><code>modelOutputWidget("M")</code></li>
-</ul></li>
-<li><code>MMainPanel()</code><br />
-</li>
-</ul></li>
-<li><code>gPanel()</code>
-<ul>
-<li><code>gSidebar()</code>
-<ul>
-<li><code>modelInputWidget("gSearchInterval")</code></li>
-<li><code>modelInputWidget("gSearchMax")</code></li>
-<li><code>modelInputWidget("useSSinputs")</code></li>
-<li><code>modelInputWidget("useSSdata")</code></li>
-<li><code>modelRunWidget("g")</code></li>
-<li><code>modelOutputWidget("g")</code></li>
-</ul></li>
-<li><code>gMainPanel()</code>
-<ul>
-<li><code>selectedDataPanel("g")</code></li>
-<li><code>modelOutputPanel("gFigures")</code></li>
-<li><code>modelOutputPanel("gSummary")</code><br />
-</li>
-</ul></li>
-</ul></li>
-</ul></li>
-<li><code>helpPanel(type)</code>
-<ul>
-<li><code>gettingStartedPanel()</code>
-<ul>
-<li><code>gettingStartedContent()</code></li>
-</ul></li>
-<li><code>downloadsPanel()</code>
-<ul>
-<li><code>dataDownloadsWidget("RP")</code></li>
-<li><code>dataDownloadsWidget("RPbat")</code></li>
-<li><code>dataDownloadsWidget("cleared")</code></li>
-<li><code>dataDownloadsWidget("powerTower")</code></li>
-<li><code>dataDownloadsWidget("PV")</code></li>
-<li><code>dataDownloadsWidget("trough")</code></li>
-<li><code>dataDownloadsWidget("mock")</code></li>
-</ul></li>
-<li><code>aboutPanel()</code>
-<ul>
-<li><code>aboutContent()</code>
-<ul>
-<li><code>GenEstAuthors()</code></li>
-<li><code>GenEstGUIauthors()</code></li>
-<li><code>GenEstLicense()</code></li>
-<li><code>GenEstAcknowledgements()</code></li>
-<li><code>GenEstLogos()</code></li>
-</ul></li>
-</ul></li>
-<li><code>disclaimersPanel(appType)</code>
-<ul>
-<li><code>disclaimerContent(appType)</code>
-<ul>
-<li><code>disclaimerUSGS()</code></li>
-<li><code>disclaimerWEST(appType)</code></li>
-</ul></li>
-</ul></li>
-</ul></li>
-</ul></li>
-</ul>
-</div>
-<div id="ui-widgets" class="section level3">
-<h3>UI Widgets</h3>
-<p>We have coded up a number of widget functions, some of which are simple wrappers on shiny functions that help reduce code clutter, and others of which are custom HTML (e.g., for model selection), but which are still nonetheless wrapped over shiny widgets:</p>
-<ul>
-<li><code>dataInputWidget(dataType)</code></li>
-<li><code>modelInputWidget(inType)</code><br />
-</li>
-<li><code>modelRunWidget(modType)</code></li>
-<li><code>modelOutputWidget(modType)</code><br />
-</li>
-<li><code>dataDownloadsWidget(set)</code></li>
-<li><code>modelSelectionWidget(modType)</code><br />
-</li>
-<li><code>kFixedWidget()</code></li>
-</ul>
-<p>A major need for widgets is having a simple condition wrapped on it, such that the widget is within a conditional panel, defined by some other input or output variable. To facilitate coding of these widgets, we have made a function <code>widgetMaker</code>, which is a generalized constructor function.</p>
-</div>
-<div id="ui-panels" class="section level3">
-<h3>UI Panels</h3>
-<p>Similarly to the input widgets, we have coded up a number of output panel functions that help direct traffic in the building on the HTML document. These functions are generalized to the suite of possible options for panels currently needed by leveraging the basic template approach with limited variation.</p>
-<ul>
-<li><code>dataTabPanel(dataType)</code></li>
-<li><code>selectedDataPanel(modType)</code></li>
-<li><code>modelOutputPanel(outType)</code></li>
-</ul>
-</div>
-<div id="ui-content" class="section level3">
-<h3>UI Content</h3>
-<p>There is a fair amount of (mostly) static content, which we have contained wihtin functions to reduce overall code clutter. These functions primarily dictate content within the “Help†tab’s subtabs.</p>
-<ul>
-<li><code>gettingStartedContent()</code></li>
-<li><code>aboutContent()</code></li>
-<li><code>GenEstAuthors()</code></li>
-<li><code>GenEstGUIAuthors()</code></li>
-<li><code>GenEstLicense()</code></li>
-<li><code>GenEstAcknowledgements()</code></li>
-<li><code>GenEstLogos()</code></li>
-<li><code>disclaimersContent(appType)</code></li>
-<li><code>disclaimerUSGS()</code></li>
-<li><code>disclaimerWEST(appType)</code></li>
-</ul>
-</div>
-</div>
-<div id="server-functionality" class="section level1">
-<h1>Server Functionality</h1>
-<div id="reactivity" class="section level3">
-<h3>Reactivity</h3>
-<p>The server-side functionality operates using <a href="https://shiny.rstudio.com/articles/reactivity-overview.html">Shiny’s reactive programming</a> framework. In particular, we include a main <code>reactiveValues</code> list called <code>rv</code>, that, in addition to the standard <code>input</code> and <code>output</code> lists is passed among functions throughout the app. The <code>rv</code> list holds all of the reactive values currently being used by the application.</p>
-</div>
-<div id="server-function-hierarchy" class="section level3">
-<h3>Server Function Hierarchy</h3>
-<p>The GenEst server code is a relatively flat hierarchy, especially in comparison to the UI code. In particular, after a brief set of preamble functions for preparing objects and options, <code>GenEstServer</code> makes many calls to <code>observeEvent</code>, the reactive observation function, one call for each of the possible events in the application (data load, model run or clear, column selection, clear of contents). Each call also includes an evaluation of the held-back “handler†code for the event returned by the function <code>reaction</code>, with the handler being the code that is to be evaluated when the event is observed. The expression is held-back in <code>reaction</code> to minimize scoping issues associated with message-related functions.</p>
-<ul>
-<li><code>GenEstServer(input, output, session)</code>
-<ul>
-<li><code>rv <- initialReactiveValues()</code></li>
-<li><code>output <- initialOutput(rv, output)</code></li>
-<li><code>msgs <- msgList()</code><br />
-</li>
-<li><code>options(htmlwidgets.TOJSON_ARGS = list(na = 'string'))</code></li>
-<li><code>observeEvent(DT.options = list(pageLength = 25))</code><br />
-</li>
-<li><code>observeEvent(input$clear_all, eval(reaction("clear_all")))</code></li>
-<li><code>observeEvent(input$file_SE, eval(reaction("file_SE")))</code></li>
-<li><code>observeEvent(input$file_SE_clear, eval(reaction("file_SE_clear")))</code></li>
-<li><code>observeEvent(input$file_CP, eval(reaction("file_CP")))</code></li>
-<li><code>observeEvent(input$file_CP_clear, eval(reaction("file_CP_clear")))</code></li>
-<li><code>observeEvent(input$file_SS, eval(reaction("file_SS")))</code></li>
-<li><code>observeEvent(input$file_SS_clear, eval(reaction("file_SS_clear")))</code></li>
-<li><code>observeEvent(input$file_DWP, eval(reaction("file_DWP")))</code></li>
-<li><code>observeEvent(input$file_DWP_clear, eval(reaction("file_DWP_clear")))</code></li>
-<li><code>observeEvent(input$file_CO, eval(reaction("file_CO")))</code></li>
-<li><p><code>observeEvent(input$file_CO_clear, eval(reaction("file_CO_clear")))</code></p></li>
-<li><p><code>observeEvent(input$class, eval(reaction("class")), ignoreNULL = FALSE)</code></p></li>
-<li><code>observeEvent(input$obsSE, eval(reaction("obsSE")), ignoreNULL = FALSE)</code></li>
-<li><code>observeEvent(input$predsSE, eval(reaction("predsSE")), ignoreNULL = FALSE)</code></li>
-<li><code>observeEvent(input$run_SE, eval(reaction("run_SE")))</code></li>
-<li><code>observeEvent(input$run_SE_clear, eval(reaction("run_SE_clear")))</code></li>
-<li><code>observeEvent(input$outSEclass, eval(reaction("outSEclass")))</code></li>
-<li><code>observeEvent(input$outSEp, eval(reaction("outSEp")))</code></li>
-<li><p><code>observeEvent(input$outSEk, eval(reaction("outSEk")))</code></p></li>
-<li><code>observeEvent(input$ltp, eval(reaction("ltp")), ignoreNULL = FALSE)</code></li>
-<li><code>observeEvent(input$fta, eval(reaction("fta")), ignoreNULL = FALSE)</code></li>
-<li><code>observeEvent(input$predsCP, eval(reaction("predsCP")), ignoreNULL = FALSE)</code></li>
-<li><code>observeEvent(input$run_CP, eval(reaction("run_CP")))</code></li>
-<li><code>observeEvent(input$run_CP_clear, eval(reaction("run_CP_clear")))</code></li>
-<li><code>observeEvent(input$outCPclass, eval(reaction("outCPclass")))</code></li>
-<li><code>observeEvent(input$outCPdist, eval(reaction("outCPdist")))</code></li>
-<li><code>observeEvent(input$outCPl, eval(reaction("outCPl")))</code></li>
-<li><p><code>observeEvent(input$outCPs, eval(reaction("outCPs")))</code></p></li>
-<li><code>observeEvent(input$run_M, eval(reaction("run_M")))</code></li>
-<li><code>observeEvent(input$run_M_clear, eval(reaction("run_M_clear")))</code></li>
-<li><code>observeEvent(input$split_M, eval(reaction("split_M")))</code></li>
-<li><code>observeEvent(input$split_M_clear, eval(reaction("split_M_clear")))</code></li>
-<li><p><code>observeEvent(input$transpose_split, eval(reaction("transpose_split")))</code></p></li>
-<li><code>observeEvent(input$useSSdata, eval(reaction("useSSdata")))</code></li>
-<li><code>observeEvent(input$useSSinputs, eval(reaction("useSSinputs")))</code></li>
-<li><code>observeEvent(input$run_g, eval(reaction("run_g")))</code></li>
-<li><code>observeEvent(input$run_g_clear, eval(reaction("run_g_clear")))</code></li>
-<li><p><code>observeEvent(input$outgclass, eval(reaction("outgclass")))</code></p></li>
-</ul></li>
-</ul>
-<p>The <code>reaction</code> function is, essentially, a parsed-text-generating function. Depending upon the specific event (<code>eventName</code>, the only input), the necessary set of function calls (messages for running, running the code, messages for when done) is prepared for evaluation. The main function used to do things within the handler code (when the event occurs) is <code>eventReaction</code>, which calls three functions: <code>update_rv</code>, <code>update_output</code>, and <code>update_input</code>, in that order (updating the output depends on the rv being updated already and updating the input requires both the rv and output to be up-to-date):</p>
-<ul>
-<li><code>eventReaction(eventName, rv, input, output, session)</code>
-<ul>
-<li><code>update_rv(eventName, rv, input)</code></li>
-<li><code>update_output(eventName, rv, output)</code></li>
-<li><code>update_input(eventName, rv, input, session)</code></li>
-</ul></li>
-</ul>
-<p>Because of the scoping set-up for Shiny apps, there is no need to assign the returned elements for <code>update_rv</code> and <code>update_output</code> to anything, and <code>update_input</code> works through <code>session</code> to direct its updates to the application.</p>
-<p>Each of the three functions takes <code>eventName</code> as the first argument, which is used to toggle amongst the possible actions to be taken with respect to each of the lists. That is, each of the three <code>update_</code> functions contains a large internal set of routines, and only the relevant ones are called for a given function. This occurs via simple conditional code blocks (“if the eventType is this, do thisâ€) for each of the possible events, thereby reducing the number of specific functions, but increasing the size of the key functions. Within each of the three functions, some of the events trigger a substantial amount of code while others only trigger a few (or no) lines. Similarly, some of the handler expressions take virutally no time to run, while others take a few minutes.</p>
-</div>
-</div>
-
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diff --git a/inst/doc/command-line-example.R b/inst/doc/command-line-example.R
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-## ----setup, include=FALSE------------------------------------------------
-knitr::opts_chunk$set(
- collapse = TRUE,
- comment = "#>"
-)
-
-## ---- include=FALSE------------------------------------------------------
-library(GenEst)
-vers <- packageVersion("GenEst")
-today <- Sys.Date()
-
-## ------------------------------------------------------------------------
-data(mock)
-names(mock)
-
-## ------------------------------------------------------------------------
-data_SE <- mock$SE
-pkModel <- pkm(formula_p = p ~ 1, formula_k = k ~ 1, data = data_SE)
-
-## ------------------------------------------------------------------------
-head(data_SE)
-
-## ------------------------------------------------------------------------
-pkModel <- pkm(formula_p = p ~ 1, formula_k = k ~ 1, data = data_SE,
- obsCol = c("Search1", "Search2", "Search3", "Search4")
- )
-
-## ------------------------------------------------------------------------
-pkModel
-
-## ------------------------------------------------------------------------
-names(pkModel)
-pkModel$cells
-
-## ---- fig.show = "hold", fig.width = 7, fig.height = 7, fig.align = 'center'----
-plot(pkModel)
-
-## ------------------------------------------------------------------------
-rpk(n = 10, pkModel)
-
-## ------------------------------------------------------------------------
-pkm(formula_p = p ~ Visibility, formula_k = k ~ HabitatType, data = data_SE,
- obsCol = c("Search1", "Search2", "Search3", "Search4")
-)
-
-## ------------------------------------------------------------------------
-pkm(formula_p = p ~ Visibility, kFixed = 0.7, data = data_SE,
- obsCol = c("Search1", "Search2", "Search3", "Search4"))
-
-## ------------------------------------------------------------------------
-pkmModSet <- pkm(formula_p = p ~ Visibility*HabitatType,
- formula_k = k ~ HabitatType, data = data_SE,
- obsCol = c("Search1", "Search2", "Search3", "Search4"),
- allCombos = TRUE
- )
-class(pkmModSet)
-names(pkmModSet)
-
-## ---- eval = F, fig.show = "hold", fig.width = 7, fig.height = 7, fig.align = 'center'----
-# plot(pkmModSet, specificModel = "p ~ Visibility + HabitatType; k ~ 1")
-
-## ------------------------------------------------------------------------
-aicc(pkmModSet)
-
-## ------------------------------------------------------------------------
-pkmModSetSize <- pkm(formula_p = p ~ Visibility*HabitatType,
- formula_k = k ~ HabitatType, data = data_SE,
- obsCol = c("Search1", "Search2", "Search3", "Search4"),
- sizeCol = "Size", allCombos = TRUE)
-class(pkmModSetSize)
-
-## ------------------------------------------------------------------------
-names(pkmModSetSize)
-names(pkmModSetSize[[1]])
-
-## ------------------------------------------------------------------------
-data_CP <- mock$CP
-cpModel <- cpm(formula_l = l ~ 1, formula_s = s ~ 1, data = data_CP,
- left = "LastPresentDecimalDays",
- right = "FirstAbsentDecimalDays", dist = "weibull"
- )
-
-## ------------------------------------------------------------------------
-cpModel
-
-## ------------------------------------------------------------------------
-names(cpModel)
-cpModel$cells
-
-## ---- fig.show = "hold", fig.width = 6, fig.height = 6, fig.align = 'center'----
-plot(cpModel)
-
-## ------------------------------------------------------------------------
-rcp(n = 10, cpModel)
-rcp(n = 10, cpModel, type = "ppersist")
-
-## ------------------------------------------------------------------------
-cpm(formula_l = l ~ Visibility * GroundCover, formula_s = s ~ 1, data = data_CP,
- left = "LastPresentDecimalDays", right = "FirstAbsentDecimalDays",
- dist = "weibull"
-)
-
-## ------------------------------------------------------------------------
-cpModExp <- cpm(formula_l = l ~ Visibility * GroundCover, data = data_CP,
- left = "LastPresentDecimalDays",
- right = "FirstAbsentDecimalDays", dist = "exponential"
- )
-
-## ------------------------------------------------------------------------
-cpmModSet <- cpm(formula_l = l ~ Visibility * Season,
- formula_s = s ~ Visibility, data = data_CP,
- left = "LastPresentDecimalDays",
- right = "FirstAbsentDecimalDays",
- dist = c("exponential", "lognormal"), allCombos = TRUE
- )
-class(cpmModSet)
-names(cpmModSet)
-
-## ------------------------------------------------------------------------
-aicc(cpmModSet)
-
-## ---- fig.show = "hold", fig.width = 7, fig.height = 7, fig.align = 'center'----
-plot(cpmModSet,
- specificModel = "dist: lognormal; l ~ Visibility * Season; s ~ Visibility"
-)
-
-## ------------------------------------------------------------------------
-cpmModSetSize <- cpm(formula_l = l ~ Visibility * Season,
- formula_s = s ~ Visibility, data = data_CP,
- left = "LastPresentDecimalDays",
- right = "FirstAbsentDecimalDays",
- dist = c("exponential", "lognormal"),
- sizeCol = "Size", allCombos = TRUE)
-class(cpmModSetSize)
-
-## ------------------------------------------------------------------------
-names(cpmModSetSize)
-names(cpmModSetSize[[1]])
-class(cpmModSetSize[[1]])
-
-## ------------------------------------------------------------------------
-pkMods <- c("S" = "p ~ 1; k ~ 1", "L" = "p ~ 1; k ~ 1",
- "M" = "p ~ 1; k ~ 1", "XL" = "p ~ 1; k ~ HabitatType"
- )
-cpMods <- c("S" = "dist: exponential; l ~ Season; NULL",
- "L" = "dist: exponential; l ~ 1; NULL",
- "M" = "dist: exponential; l ~ 1; NULL",
- "XL" = "dist: exponential; l ~ 1; NULL"
- )
-
-## ------------------------------------------------------------------------
-data_SS <- mock$SS
-avgSS <- averageSS(data_SS)
-
-gsGeneric <- estgGenericSize(nsim = 1000, days = avgSS,
- modelSetSize_SE = pkmModSetSize,
- modelSetSize_CP = cpmModSetSize,
- modelSizeSelections_SE = pkMods,
- modelSizeSelections_CP = cpMods
- )
-
-## ------------------------------------------------------------------------
-summary(gsGeneric)
-
-## ---- fig.show = "hold", fig.width = 4, fig.height = 14, fig.align = 'center'----
-plot(gsGeneric)
-
-## ------------------------------------------------------------------------
-pkmModSize <- trimSetSize(pkmModSetSize, pkMods)
-cpmModSize <- trimSetSize(cpmModSetSize, cpMods)
-
-## ------------------------------------------------------------------------
-data_CO <- mock$CO
-data_DWP <- mock$DWP
-head(data_DWP)
-DWPcolnames <- names(pkmModSize)
-
-eM <- estM(data_CO = data_CO, data_SS = data_SS, data_DWP = data_DWP,
- frac = 1, model_SE = pkmModSize, model_CP = cpmModSize,
- seed_SE = NULL, seed_CP = NULL, seed_g = NULL, seed_M = NULL,
- unitCol = "Unit", COdate = "DateFound",
- SSdate = "DateSearched", sizeCol = "Size", nsim = 1000)
-
-## ---- fig.show = "hold", fig.width = 6, fig.height = 6, fig.align = 'center'----
-summary(eM)
-plot(eM)
-
-## ---- fig.show = "hold", fig.width = 4, fig.height = 6, fig.align = 'center'----
-M_season <- calcSplits(M = eM, split_SS = "Construction",
- split_CO = NULL, data_SS = data_SS, data_CO = data_CO
- )
-summary(M_season)
-plot(M_season)
-
-## ---- fig.show = "hold", fig.width = 4, fig.height = 6, fig.align = 'center'----
-M_class <- calcSplits(M = eM, split_SS = NULL,
- split_CO = "Split", data_SS = data_SS, data_CO = data_CO
- )
-summary(M_class)
-plot(M_class)
-
-## ---- fig.show = "hold", fig.width = 4, fig.height = 8, fig.align = 'center'----
-M_SbyC <- calcSplits(M = eM, split_SS = "Construction",
- split_CO = "Split", data_SS = data_SS, data_CO = data_CO
- )
-summary(M_SbyC)
-plot(M_SbyC)
-
diff --git a/inst/doc/command-line-example.Rmd b/inst/doc/command-line-example.Rmd
deleted file mode 100644
index 81110cb440f3c14305b4f7bf40ffea5ce77c5bf8..0000000000000000000000000000000000000000
--- a/inst/doc/command-line-example.Rmd
+++ /dev/null
@@ -1,491 +0,0 @@
----
-title: "GenEst - Command Line Walkthrough"
-author: "Juniper L. Simonis"
-date: "`r Sys.Date()`"
-output: rmarkdown::html_vignette
-vignette: >
- %\VignetteIndexEntry{GenEst - 3. Command Line Walkthrough}
- %\VignetteEngine{knitr::rmarkdown}
- \usepackage[utf8]{inputenc}
----
-
-```{r setup, include=FALSE}
-knitr::opts_chunk$set(
- collapse = TRUE,
- comment = "#>"
-)
-```
-
-```{r, include=FALSE}
-library(GenEst)
-vers <- packageVersion("GenEst")
-today <- Sys.Date()
-```
-
-This vignette walks through an example of **GenEst** at the command line and
-was constructed using **GenEst** version `r vers` on `r today`.
-
-
-## Installation
-
-To obtain the most recent version of **GenEst**, download the most recent
-version build from
-[GitHub](https://github.com/ddalthorp/GenEst/tree/master/)
-
-## Data
-
-For this vignette, we will be using a completely generic, mock dataset provided
-with the **GenEst** package, which contains Searcher Efficiency (SE), Carcass
-Persistence (CP), Search Schedule (SS), Density Weighted Proportion (DWP), and
-Carcass Observation (CO) Data.
-
-```{r}
-data(mock)
-names(mock)
-```
-
-## Searcher Efficiency
-
-### Single Searcher Efficiency Model
-
-The central function for searcher efficiency analyses is `pkm`, which, in its
-most basic form, conducts a singular searcher efficiency analysis (*i.e.*, a
-singular set of $p$ and $k$ formulae and a singular size classification of
-carcasses). As a first example, we will ignore the size category and use
-intercept-only models for both $p$ and $k$:
-
-```{r}
-data_SE <- mock$SE
-pkModel <- pkm(formula_p = p ~ 1, formula_k = k ~ 1, data = data_SE)
-```
-
-Here, we have taken advantage of `pkm`'s default behavior of selecting
-observation columns (see `?pkm` for details).
-
-```{r}
-head(data_SE)
-```
-
-If we wanted to explicitly control the observations, we would use the `obsCol`
-argument:
-
-```{r}
-pkModel <- pkm(formula_p = p ~ 1, formula_k = k ~ 1, data = data_SE,
- obsCol = c("Search1", "Search2", "Search3", "Search4")
- )
-```
-
-Note that the search observations must be entered in order such that no
-carcasses have non-detected observations (*i.e.*, `0`) after detected
-observations (*i.e.*, `1`). Further, no carcasses can be detected more than
-once.
-
-If successfully fit, a `pkm` model output contains a number of elements,
-some printed automatically:
-
-```{r}
-pkModel
-```
-
-and others available upon request:
-
-```{r}
-names(pkModel)
-pkModel$cells
-```
-
-The `plot` function has been defined for `pkm` objects, such that one can
-simply run
-
-```{r, fig.show = "hold", fig.width = 7, fig.height = 7, fig.align = 'center'}
-plot(pkModel)
-```
-
-to visualize the model's output.
-
-You can generate random draws of the $p$ and $k$ parameters for each cell
-grouping (in `pkModel` there are no predictors, so there is one cell grouping
-called "all") using the `rpk` function which, like other `r*` functions in
-**R** (*e.g.*, `rnorm`, `runif`) takes the number of random draws (`n`) as the
-first argument:
-
-```{r}
-rpk(n = 10, pkModel)
-```
-
-You can complicate the $p$ and $k$ formulae independently
-
-```{r}
-pkm(formula_p = p ~ Visibility, formula_k = k ~ HabitatType, data = data_SE,
- obsCol = c("Search1", "Search2", "Search3", "Search4")
-)
-```
-
-And you can fix $k$ at a nominal value between 0 and 1 (inclusive) using the
-`kFixed` argument
-
-```{r}
-pkm(formula_p = p ~ Visibility, kFixed = 0.7, data = data_SE,
- obsCol = c("Search1", "Search2", "Search3", "Search4"))
-```
-
-### Set of Searcher Efficiency Models
-
-If the arg `allCombos = TRUE` is provided, `pkm` fits a set of `pkm` models
-defined as all allowable models simpler than, and including, the provided model
-for both formulae (where "allowable" means that any interaction terms have all
-component terms included in the model).
-
-Consider the following model set analysis, where visibility and habitat type
-are included in the $p$ formula but only habitat type is in the $k$ formula.
-This generates a set of 10 models:
-
-```{r}
-pkmModSet <- pkm(formula_p = p ~ Visibility*HabitatType,
- formula_k = k ~ HabitatType, data = data_SE,
- obsCol = c("Search1", "Search2", "Search3", "Search4"),
- allCombos = TRUE
- )
-class(pkmModSet)
-names(pkmModSet)
-```
-
-The `plot` function is defined for the `pkmSet` class, and by default,
-creates a new plot window on command for each sub-model. If we want to only
-plot a specific single (or subset) of models from the full set, we can utilize
-the `specificModel` argument:
-
-```{r, eval = F, fig.show = "hold", fig.width = 7, fig.height = 7, fig.align = 'center'}
-plot(pkmModSet, specificModel = "p ~ Visibility + HabitatType; k ~ 1")
-```
-
-The resulting model outputs can be compared in an AICc table
-
-```{r}
-aicc(pkmModSet)
-```
-
-
-### Multiple Sizes of Animals and Sets of Searcher Efficiency Models
-
-Often, carcasses are grouped in multiple size classes, and we are interested
-in analyzing a set of models separately for each size class. To do so, we
-use the `sizeCol` arg to tell `pkm` which column in `data_CP` gives the carcass
-size class. If, in addition, `allCombos = TRUE`, `pkm` will fit a `pkmSet` that
-runs for each unique size class in the column identified by the `sizeCol`
-argument:
-
-```{r}
-pkmModSetSize <- pkm(formula_p = p ~ Visibility*HabitatType,
- formula_k = k ~ HabitatType, data = data_SE,
- obsCol = c("Search1", "Search2", "Search3", "Search4"),
- sizeCol = "Size", allCombos = TRUE)
-class(pkmModSetSize)
-```
-
-The `pkmSetSize` object is a list where each element corresponds to a
-different unique size class, and contains the associated `pkmSet`object, which
-itself is a list of `pkm` outputs:
-
-```{r}
-names(pkmModSetSize)
-names(pkmModSetSize[[1]])
-```
-
-
-## Carcass Persistence
-
-### Single Carcass Persistence Model
-
-The central function for carcass persistence analyses is `cpm`, which, in its
-simplest form, conducts a singular carcass persistence analysis (*i.e.*, a
-singular set of $l$ and $s$ formulae and a singular size classification of
-carcasses). Note that we use $l$ and $s$ to reference $location$ and $scale$
-as the parameters for survival models, following `survreg`, however we also
-provide an alternative parameterization (using parameters $a$ and $b$,
-referred to as "`ab`" or "ppersist"). As a first example, we will ignore the
-size category, use intercept-only models for both $l$ and $s$, and use the
-Weibull distribution:
-
-```{r}
-data_CP <- mock$CP
-cpModel <- cpm(formula_l = l ~ 1, formula_s = s ~ 1, data = data_CP,
- left = "LastPresentDecimalDays",
- right = "FirstAbsentDecimalDays", dist = "weibull"
- )
-```
-
-
-If successfully fit, a `cpm` model output contains a number of elements,
-some printed automatically:
-
-```{r}
-cpModel
-```
-
-and others available upon request:
-
-```{r}
-names(cpModel)
-cpModel$cells
-```
-
-The `plot` function has been defined for `cpm` objects, such that one can
-simply run
-
-```{r, fig.show = "hold", fig.width = 6, fig.height = 6, fig.align = 'center'}
-plot(cpModel)
-```
-
-to visualize the model's output.
-
-You can generate random draws of the $l$ and $s$ (or $a$ and $b$) parameters
-for each cell grouping (in `cpModel` there are no predictors, so there is one
-cell grouping called "all") using the `rcp` function which, like other `r*`
-functions in **R** (*e.g.*, `rnorm`) takes the number of random draws (`n`)
-as the first argument:
-
-```{r}
-rcp(n = 10, cpModel)
-rcp(n = 10, cpModel, type = "ppersist")
-```
-
-You can complicate the $l$ and $s$ formulae independently
-
-```{r}
-cpm(formula_l = l ~ Visibility * GroundCover, formula_s = s ~ 1, data = data_CP,
- left = "LastPresentDecimalDays", right = "FirstAbsentDecimalDays",
- dist = "weibull"
-)
-```
-
-Given that the exponential only has one parameter ($l$, location), a model
-for scale (`formula_s`) is not required:
-
-```{r}
-cpModExp <- cpm(formula_l = l ~ Visibility * GroundCover, data = data_CP,
- left = "LastPresentDecimalDays",
- right = "FirstAbsentDecimalDays", dist = "exponential"
- )
-```
-
-### Set of Carcass Persistence Models
-
-If the arg `allCombos = TRUE` is provided, `cpm` fits a set of `cpm` models
-defined as all allowable models simpler than, and including, the provided model
-formulae (where "allowable" means that any interaction terms have all component
-terms included in the model).
-
-In addition, `cpm` with `allCombos` can include any subset of the four base
-distributions (exponential, weibull, lognormal, loglogistic) and crosses them
-with the predictor models.
-
-Consider the following model set analysis, where `Visibility` and `Season` are
-included in the $l$ formula but only `Visibility` is in the $s$ formula, and
-only the exponential and lognormal distributions are included. This generates a
-set of 15 models:
-
-```{r}
-cpmModSet <- cpm(formula_l = l ~ Visibility * Season,
- formula_s = s ~ Visibility, data = data_CP,
- left = "LastPresentDecimalDays",
- right = "FirstAbsentDecimalDays",
- dist = c("exponential", "lognormal"), allCombos = TRUE
- )
-class(cpmModSet)
-names(cpmModSet)
-```
-
-The resulting model outputs can be compared in an AICc table
-
-```{r}
-aicc(cpmModSet)
-```
-
-The `plot` function is defined for the `cpmSet` class, and by default,
-creates a new plot window on command for each sub-model. If we want to only
-plot a specific single (or subset) of models from the full set, we can utilize
-the `specificModel` argument:
-
-```{r, fig.show = "hold", fig.width = 7, fig.height = 7, fig.align = 'center'}
-plot(cpmModSet,
- specificModel = "dist: lognormal; l ~ Visibility * Season; s ~ Visibility"
-)
-```
-
-### Multiple Sizes of Animals and Sets of Carcass Persistence Models
-
-Often, carcasses are grouped in multiple size classes, and we are interested
-in analyzing a set of models separately for each size class. To do so, we
-furnish `cpm` with `sizeCol`, which is the name of the column in `data_CP` that
-gives the size classes of the carcasses. If, in addition, `allCombos = TRUE`,
-then `cpm` returns a `cpmSet` for each unique size class in the column
-identified by the `sizeCol` argument:
-
-```{r}
-cpmModSetSize <- cpm(formula_l = l ~ Visibility * Season,
- formula_s = s ~ Visibility, data = data_CP,
- left = "LastPresentDecimalDays",
- right = "FirstAbsentDecimalDays",
- dist = c("exponential", "lognormal"),
- sizeCol = "Size", allCombos = TRUE)
-class(cpmModSetSize)
-```
-
-The `cpmSetSize` object is a list where each element corresponds to a
-different unique size class, and contains the associated `cpmSet`o bject, which
-itself is a list of `cpm` outputs:
-
-```{r}
-names(cpmModSetSize)
-names(cpmModSetSize[[1]])
-class(cpmModSetSize[[1]])
-```
-
-## Generic Detection Probability
-
-For the purposes of mortality estimation, we calculate carcass-specific
-detection probabilities (see below), which may be difficult to generalize,
-given the specific history of each observed carcass. Thus, we also provide
-a simple means to calculate generic detection probabilities that are
-cell-specific, rather than carcass-specific.
-
-For any estimation of detection probability ($\hat{g}$), we need to have
-singular SE and CP models to use for each of the size classes. Here, we use
-the best-fit of the models for each size class:
-
-```{r}
-pkMods <- c("S" = "p ~ 1; k ~ 1", "L" = "p ~ 1; k ~ 1",
- "M" = "p ~ 1; k ~ 1", "XL" = "p ~ 1; k ~ HabitatType"
- )
-cpMods <- c("S" = "dist: exponential; l ~ Season; NULL",
- "L" = "dist: exponential; l ~ 1; NULL",
- "M" = "dist: exponential; l ~ 1; NULL",
- "XL" = "dist: exponential; l ~ 1; NULL"
- )
-```
-
-The `estgGenericSize` function produces `n` random draws of generic (i.e.,
-cell-specific, not carcass-sepecific) detection probabilities for
-each of the possible carcass cell combinations across the selected SE and CP
-models across the size classes. `estgGeneric` is a single-size-class
-version of function and `estgGenericSize` actually loops over
-`estgGeneric`. The generic $\hat{g}$ is estimated according to a particular
-search schedule. When we pass `averageSS` a full `data_SS` table like we have
-here, it will assume that columns filled exclusively with 0s and 1s represent
-search schedules for units and will create the average search schedule across
-the units.
-
-```{r}
-data_SS <- mock$SS
-avgSS <- averageSS(data_SS)
-
-gsGeneric <- estgGenericSize(nsim = 1000, days = avgSS,
- modelSetSize_SE = pkmModSetSize,
- modelSetSize_CP = cpmModSetSize,
- modelSizeSelections_SE = pkMods,
- modelSizeSelections_CP = cpMods
- )
-```
-
-The output from `estgGeneric` can be simply summarized
-```{r}
-summary(gsGeneric)
-```
-
-or plotted.
-
-```{r, fig.show = "hold", fig.width = 4, fig.height = 14, fig.align = 'center'}
-plot(gsGeneric)
-```
-
-
-## Mortality Estimation
-
-When estimating mortality, detection probability is determined for
-individual carcasses based on the dates when they are observed, size class
-values, associated covariates, the searcher efficiency and carcass persistence
-models, and the search schedule. The carcass-specific detection probabilities
-(as opposed to the generic/cell-specific detection probabilities above) are
-therefore calculated before estimating the total mortality.
-Although it is possible to estimate these detection probabilities separately,
-they are best interpreted in the context of a full mortality estimation.
-
-The `estM` function is the general wrapper function for estimating `M`,
-whether for a single size class or multiple size classes. Prior to estimation,
-we need to reduce the model-set-size complexed to just a single chosen
-model per size class, corresponding to the `pkMods` and `cpMods` vectors
-given above. To reduce the model set complexity, we can use the `trimSetSize`
-function:
-
-```{r}
-pkmModSize <- trimSetSize(pkmModSetSize, pkMods)
-cpmModSize <- trimSetSize(cpmModSetSize, cpMods)
-```
-
-In addition to the models and search schedule data, `estM` requires
-density-weighted proportion (DWP) and carcass observation (CO) data. If more
-than one size class is represented in the data, a required input is also the
-column names associated with the DWP value for each size class (argument
-`DWPCol` in `estM`):
-
-```{r}
-data_CO <- mock$CO
-data_DWP <- mock$DWP
-head(data_DWP)
-DWPcolnames <- names(pkmModSize)
-
-eM <- estM(data_CO = data_CO, data_SS = data_SS, data_DWP = data_DWP,
- frac = 1, model_SE = pkmModSize, model_CP = cpmModSize,
- seed_SE = NULL, seed_CP = NULL, seed_g = NULL, seed_M = NULL,
- unitCol = "Unit", COdate = "DateFound",
- SSdate = "DateSearched", sizeCol = "Size", nsim = 1000)
-```
-
-`estM` returns an object that contains the random draws of `pkm` and `cpm`
-parameters (named `pk` and `ab`, respectively) and the estimated carcass-level
-detection parameters (`g`), arrival intervals (`Aj`), and associated total
-mortality (`Mhat`) values for each simulation. These `Mhat` values should be
-considered in combination, and can be summarized and plotted simply:
-
-```{r, fig.show = "hold", fig.width = 6, fig.height = 6, fig.align = 'center'}
-summary(eM)
-plot(eM)
-```
-
-### Splitting Mortality Estimations
-
-It is possible to split the resulting mortality estimation into components
-that are denoted according to covariates in either the search schedule or
-carcass observation data sets.
-
-First, a temporal split:
-
-```{r, fig.show = "hold", fig.width = 4, fig.height = 6, fig.align = 'center'}
-M_season <- calcSplits(M = eM, split_SS = "Construction",
- split_CO = NULL, data_SS = data_SS, data_CO = data_CO
- )
-summary(M_season)
-plot(M_season)
-```
-
-Next, a carcass split:
-
-```{r, fig.show = "hold", fig.width = 4, fig.height = 6, fig.align = 'center'}
-M_class <- calcSplits(M = eM, split_SS = NULL,
- split_CO = "Split", data_SS = data_SS, data_CO = data_CO
- )
-summary(M_class)
-plot(M_class)
-```
-
-And finally, if two splits are included, the mortality estimation is expanded
-fully factorially:
-
-```{r, fig.show = "hold", fig.width = 4, fig.height = 8, fig.align = 'center'}
-M_SbyC <- calcSplits(M = eM, split_SS = "Construction",
- split_CO = "Split", data_SS = data_SS, data_CO = data_CO
- )
-summary(M_SbyC)
-plot(M_SbyC)
-```
\ No newline at end of file
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-<h1 class="title toc-ignore">GenEst - Command Line Walkthrough</h1>
-<h4 class="author"><em>Juniper L. Simonis</em></h4>
-<h4 class="date"><em>2018-12-05</em></h4>
-
-
-
-<p>This vignette walks through an example of <strong>GenEst</strong> at the command line and was constructed using <strong>GenEst</strong> version 1.1.4 on 2018-12-05.</p>
-<div id="installation" class="section level2">
-<h2>Installation</h2>
-<p>To obtain the most recent version of <strong>GenEst</strong>, download the most recent version build from <a href="https://github.com/ddalthorp/GenEst/tree/master/">GitHub</a></p>
-</div>
-<div id="data" class="section level2">
-<h2>Data</h2>
-<p>For this vignette, we will be using a completely generic, mock dataset provided with the <strong>GenEst</strong> package, which contains Searcher Efficiency (SE), Carcass Persistence (CP), Search Schedule (SS), Density Weighted Proportion (DWP), and Carcass Observation (CO) Data.</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw">data</span>(mock)</a>
-<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw">names</span>(mock)</a>
-<a class="sourceLine" id="cb1-3" data-line-number="3"><span class="co">#> [1] "SE" "CP" "SS" "DWP" "CO"</span></a></code></pre></div>
-</div>
-<div id="searcher-efficiency" class="section level2">
-<h2>Searcher Efficiency</h2>
-<div id="single-searcher-efficiency-model" class="section level3">
-<h3>Single Searcher Efficiency Model</h3>
-<p>The central function for searcher efficiency analyses is <code>pkm</code>, which, in its most basic form, conducts a singular searcher efficiency analysis (<em>i.e.</em>, a singular set of <span class="math inline">\(p\)</span> and <span class="math inline">\(k\)</span> formulae and a singular size classification of carcasses). As a first example, we will ignore the size category and use intercept-only models for both <span class="math inline">\(p\)</span> and <span class="math inline">\(k\)</span>:</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1">data_SE <-<span class="st"> </span>mock<span class="op">$</span>SE</a>
-<a class="sourceLine" id="cb2-2" data-line-number="2">pkModel <-<span class="st"> </span><span class="kw">pkm</span>(<span class="dt">formula_p =</span> p <span class="op">~</span><span class="st"> </span><span class="dv">1</span>, <span class="dt">formula_k =</span> k <span class="op">~</span><span class="st"> </span><span class="dv">1</span>, <span class="dt">data =</span> data_SE)</a></code></pre></div>
-<p>Here, we have taken advantage of <code>pkm</code>’s default behavior of selecting observation columns (see <code>?pkm</code> for details).</p>
-<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1"><span class="kw">head</span>(data_SE)</a>
-<a class="sourceLine" id="cb3-2" data-line-number="2"><span class="co">#> seID Visibility HabitatType Season Size Search1 Search2 Search3 Search4</span></a>
-<a class="sourceLine" id="cb3-3" data-line-number="3"><span class="co">#> 1 se1 L HT1 SF S 1 NA NA NA</span></a>
-<a class="sourceLine" id="cb3-4" data-line-number="4"><span class="co">#> 2 se2 L HT1 SF S 1 NA NA NA</span></a>
-<a class="sourceLine" id="cb3-5" data-line-number="5"><span class="co">#> 3 se3 L HT1 WS S 0 0 0 0</span></a>
-<a class="sourceLine" id="cb3-6" data-line-number="6"><span class="co">#> 4 se4 L HT1 WS S 1 NA NA NA</span></a>
-<a class="sourceLine" id="cb3-7" data-line-number="7"><span class="co">#> 5 se5 L HT1 WS S 0 1 NA NA</span></a>
-<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="co">#> 6 se6 L HT1 SF S 1 NA NA NA</span></a></code></pre></div>
-<p>If we wanted to explicitly control the observations, we would use the <code>obsCol</code> argument:</p>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1">pkModel <-<span class="st"> </span><span class="kw">pkm</span>(<span class="dt">formula_p =</span> p <span class="op">~</span><span class="st"> </span><span class="dv">1</span>, <span class="dt">formula_k =</span> k <span class="op">~</span><span class="st"> </span><span class="dv">1</span>, <span class="dt">data =</span> data_SE,</a>
-<a class="sourceLine" id="cb4-2" data-line-number="2"> <span class="dt">obsCol =</span> <span class="kw">c</span>(<span class="st">"Search1"</span>, <span class="st">"Search2"</span>, <span class="st">"Search3"</span>, <span class="st">"Search4"</span>)</a>
-<a class="sourceLine" id="cb4-3" data-line-number="3"> )</a></code></pre></div>
-<p>Note that the search observations must be entered in order such that no carcasses have non-detected observations (<em>i.e.</em>, <code>0</code>) after detected observations (<em>i.e.</em>, <code>1</code>). Further, no carcasses can be detected more than once.</p>
-<p>If successfully fit, a <code>pkm</code> model output contains a number of elements, some printed automatically:</p>
-<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1">pkModel</a>
-<a class="sourceLine" id="cb5-2" data-line-number="2"><span class="co">#> $call</span></a>
-<a class="sourceLine" id="cb5-3" data-line-number="3"><span class="co">#> pkm0(formula_p = formula_p, formula_k = formula_k, data = data, </span></a>
-<a class="sourceLine" id="cb5-4" data-line-number="4"><span class="co">#> obsCol = obsCol, kFixed = kFixed, kInit = kInit, CL = CL, </span></a>
-<a class="sourceLine" id="cb5-5" data-line-number="5"><span class="co">#> quiet = quiet)</span></a>
-<a class="sourceLine" id="cb5-6" data-line-number="6"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb5-7" data-line-number="7"><span class="co">#> $formula_p</span></a>
-<a class="sourceLine" id="cb5-8" data-line-number="8"><span class="co">#> p ~ 1</span></a>
-<a class="sourceLine" id="cb5-9" data-line-number="9"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb5-10" data-line-number="10"><span class="co">#> $formula_k</span></a>
-<a class="sourceLine" id="cb5-11" data-line-number="11"><span class="co">#> k ~ 1</span></a>
-<a class="sourceLine" id="cb5-12" data-line-number="12"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb5-13" data-line-number="13"><span class="co">#> $predictors</span></a>
-<a class="sourceLine" id="cb5-14" data-line-number="14"><span class="co">#> character(0)</span></a>
-<a class="sourceLine" id="cb5-15" data-line-number="15"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb5-16" data-line-number="16"><span class="co">#> $AICc</span></a>
-<a class="sourceLine" id="cb5-17" data-line-number="17"><span class="co">#> [1] 1145</span></a>
-<a class="sourceLine" id="cb5-18" data-line-number="18"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb5-19" data-line-number="19"><span class="co">#> $convergence</span></a>
-<a class="sourceLine" id="cb5-20" data-line-number="20"><span class="co">#> [1] 0</span></a>
-<a class="sourceLine" id="cb5-21" data-line-number="21"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb5-22" data-line-number="22"><span class="co">#> $cell_pk</span></a>
-<a class="sourceLine" id="cb5-23" data-line-number="23"><span class="co">#> cell n p_median p_lower p_upper k_median k_lower k_upper</span></a>
-<a class="sourceLine" id="cb5-24" data-line-number="24"><span class="co">#> 1 all 480 0.568 0.532 0.604 0.599 0.543 0.653</span></a>
-<a class="sourceLine" id="cb5-25" data-line-number="25"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb5-26" data-line-number="26"><span class="co">#> $CL</span></a>
-<a class="sourceLine" id="cb5-27" data-line-number="27"><span class="co">#> [1] 0.9</span></a></code></pre></div>
-<p>and others available upon request:</p>
-<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1"><span class="kw">names</span>(pkModel)</a>
-<a class="sourceLine" id="cb6-2" data-line-number="2"><span class="co">#> [1] "call" "data" "data0" "formula_p" </span></a>
-<a class="sourceLine" id="cb6-3" data-line-number="3"><span class="co">#> [5] "formula_k" "predictors" "predictors_p" "predictors_k"</span></a>
-<a class="sourceLine" id="cb6-4" data-line-number="4"><span class="co">#> [9] "AIC" "AICc" "convergence" "varbeta" </span></a>
-<a class="sourceLine" id="cb6-5" data-line-number="5"><span class="co">#> [13] "cellMM_p" "cellMM_k" "nbeta_p" "nbeta_k" </span></a>
-<a class="sourceLine" id="cb6-6" data-line-number="6"><span class="co">#> [17] "betahat_p" "betahat_k" "cells" "ncell" </span></a>
-<a class="sourceLine" id="cb6-7" data-line-number="7"><span class="co">#> [21] "cell_pk" "CL" "observations" "carcCells" </span></a>
-<a class="sourceLine" id="cb6-8" data-line-number="8"><span class="co">#> [25] "loglik" "pOnly"</span></a>
-<a class="sourceLine" id="cb6-9" data-line-number="9">pkModel<span class="op">$</span>cells</a>
-<a class="sourceLine" id="cb6-10" data-line-number="10"><span class="co">#> group CellNames</span></a>
-<a class="sourceLine" id="cb6-11" data-line-number="11"><span class="co">#> 1 all all</span></a></code></pre></div>
-<p>The <code>plot</code> function has been defined for <code>pkm</code> objects, such that one can simply run</p>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1"><span class="kw">plot</span>(pkModel)</a></code></pre></div>
-<p><img 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" style="display: block; margin: auto;" /></p>
-<p>to visualize the model’s output.</p>
-<p>You can generate random draws of the <span class="math inline">\(p\)</span> and <span class="math inline">\(k\)</span> parameters for each cell grouping (in <code>pkModel</code> there are no predictors, so there is one cell grouping called “allâ€) using the <code>rpk</code> function which, like other <code>r*</code> functions in <strong>R</strong> (<em>e.g.</em>, <code>rnorm</code>, <code>runif</code>) takes the number of random draws (<code>n</code>) as the first argument:</p>
-<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" data-line-number="1"><span class="kw">rpk</span>(<span class="dt">n =</span> <span class="dv">10</span>, pkModel)</a>
-<a class="sourceLine" id="cb8-2" data-line-number="2"><span class="co">#> $all</span></a>
-<a class="sourceLine" id="cb8-3" data-line-number="3"><span class="co">#> p k</span></a>
-<a class="sourceLine" id="cb8-4" data-line-number="4"><span class="co">#> [1,] 0.5757552 0.6111124</span></a>
-<a class="sourceLine" id="cb8-5" data-line-number="5"><span class="co">#> [2,] 0.5485472 0.6191553</span></a>
-<a class="sourceLine" id="cb8-6" data-line-number="6"><span class="co">#> [3,] 0.5607189 0.6520001</span></a>
-<a class="sourceLine" id="cb8-7" data-line-number="7"><span class="co">#> [4,] 0.5699556 0.6269243</span></a>
-<a class="sourceLine" id="cb8-8" data-line-number="8"><span class="co">#> [5,] 0.6037388 0.5905767</span></a>
-<a class="sourceLine" id="cb8-9" data-line-number="9"><span class="co">#> [6,] 0.5999656 0.5577046</span></a>
-<a class="sourceLine" id="cb8-10" data-line-number="10"><span class="co">#> [7,] 0.5676859 0.6584631</span></a>
-<a class="sourceLine" id="cb8-11" data-line-number="11"><span class="co">#> [8,] 0.5695743 0.5578731</span></a>
-<a class="sourceLine" id="cb8-12" data-line-number="12"><span class="co">#> [9,] 0.5708306 0.5635206</span></a>
-<a class="sourceLine" id="cb8-13" data-line-number="13"><span class="co">#> [10,] 0.5964129 0.6001012</span></a></code></pre></div>
-<p>You can complicate the <span class="math inline">\(p\)</span> and <span class="math inline">\(k\)</span> formulae independently</p>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" data-line-number="1"><span class="kw">pkm</span>(<span class="dt">formula_p =</span> p <span class="op">~</span><span class="st"> </span>Visibility, <span class="dt">formula_k =</span> k <span class="op">~</span><span class="st"> </span>HabitatType, <span class="dt">data =</span> data_SE,</a>
-<a class="sourceLine" id="cb9-2" data-line-number="2"> <span class="dt">obsCol =</span> <span class="kw">c</span>(<span class="st">"Search1"</span>, <span class="st">"Search2"</span>, <span class="st">"Search3"</span>, <span class="st">"Search4"</span>)</a>
-<a class="sourceLine" id="cb9-3" data-line-number="3">)</a>
-<a class="sourceLine" id="cb9-4" data-line-number="4"><span class="co">#> $call</span></a>
-<a class="sourceLine" id="cb9-5" data-line-number="5"><span class="co">#> pkm0(formula_p = formula_p, formula_k = formula_k, data = data, </span></a>
-<a class="sourceLine" id="cb9-6" data-line-number="6"><span class="co">#> obsCol = obsCol, kFixed = kFixed, kInit = kInit, CL = CL, </span></a>
-<a class="sourceLine" id="cb9-7" data-line-number="7"><span class="co">#> quiet = quiet)</span></a>
-<a class="sourceLine" id="cb9-8" data-line-number="8"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb9-9" data-line-number="9"><span class="co">#> $formula_p</span></a>
-<a class="sourceLine" id="cb9-10" data-line-number="10"><span class="co">#> p ~ Visibility</span></a>
-<a class="sourceLine" id="cb9-11" data-line-number="11"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb9-12" data-line-number="12"><span class="co">#> $formula_k</span></a>
-<a class="sourceLine" id="cb9-13" data-line-number="13"><span class="co">#> k ~ HabitatType</span></a>
-<a class="sourceLine" id="cb9-14" data-line-number="14"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb9-15" data-line-number="15"><span class="co">#> $predictors</span></a>
-<a class="sourceLine" id="cb9-16" data-line-number="16"><span class="co">#> [1] "Visibility" "HabitatType"</span></a>
-<a class="sourceLine" id="cb9-17" data-line-number="17"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb9-18" data-line-number="18"><span class="co">#> $AICc</span></a>
-<a class="sourceLine" id="cb9-19" data-line-number="19"><span class="co">#> [1] 1149.67</span></a>
-<a class="sourceLine" id="cb9-20" data-line-number="20"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb9-21" data-line-number="21"><span class="co">#> $convergence</span></a>
-<a class="sourceLine" id="cb9-22" data-line-number="22"><span class="co">#> [1] 0</span></a>
-<a class="sourceLine" id="cb9-23" data-line-number="23"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb9-24" data-line-number="24"><span class="co">#> $cell_pk</span></a>
-<a class="sourceLine" id="cb9-25" data-line-number="25"><span class="co">#> cell n p_median p_lower p_upper k_median k_lower k_upper</span></a>
-<a class="sourceLine" id="cb9-26" data-line-number="26"><span class="co">#> 1 H.HT1 80 0.564 0.503 0.623 0.560 0.482 0.636</span></a>
-<a class="sourceLine" id="cb9-27" data-line-number="27"><span class="co">#> 2 L.HT1 80 0.578 0.516 0.637 0.560 0.482 0.636</span></a>
-<a class="sourceLine" id="cb9-28" data-line-number="28"><span class="co">#> 3 M.HT1 80 0.563 0.505 0.620 0.560 0.482 0.636</span></a>
-<a class="sourceLine" id="cb9-29" data-line-number="29"><span class="co">#> 4 H.HT2 80 0.564 0.503 0.623 0.631 0.557 0.700</span></a>
-<a class="sourceLine" id="cb9-30" data-line-number="30"><span class="co">#> 5 L.HT2 80 0.578 0.516 0.637 0.631 0.557 0.700</span></a>
-<a class="sourceLine" id="cb9-31" data-line-number="31"><span class="co">#> 6 M.HT2 80 0.563 0.505 0.620 0.631 0.557 0.700</span></a>
-<a class="sourceLine" id="cb9-32" data-line-number="32"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb9-33" data-line-number="33"><span class="co">#> $CL</span></a>
-<a class="sourceLine" id="cb9-34" data-line-number="34"><span class="co">#> [1] 0.9</span></a></code></pre></div>
-<p>And you can fix <span class="math inline">\(k\)</span> at a nominal value between 0 and 1 (inclusive) using the <code>kFixed</code> argument</p>
-<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" data-line-number="1"><span class="kw">pkm</span>(<span class="dt">formula_p =</span> p <span class="op">~</span><span class="st"> </span>Visibility, <span class="dt">kFixed =</span> <span class="fl">0.7</span>, <span class="dt">data =</span> data_SE,</a>
-<a class="sourceLine" id="cb10-2" data-line-number="2"> <span class="dt">obsCol =</span> <span class="kw">c</span>(<span class="st">"Search1"</span>, <span class="st">"Search2"</span>, <span class="st">"Search3"</span>, <span class="st">"Search4"</span>))</a>
-<a class="sourceLine" id="cb10-3" data-line-number="3"><span class="co">#> $call</span></a>
-<a class="sourceLine" id="cb10-4" data-line-number="4"><span class="co">#> pkm0(formula_p = formula_p, formula_k = formula_k, data = data, </span></a>
-<a class="sourceLine" id="cb10-5" data-line-number="5"><span class="co">#> obsCol = obsCol, kFixed = kFixed, kInit = kInit, CL = CL, </span></a>
-<a class="sourceLine" id="cb10-6" data-line-number="6"><span class="co">#> quiet = quiet)</span></a>
-<a class="sourceLine" id="cb10-7" data-line-number="7"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb10-8" data-line-number="8"><span class="co">#> $formula_p</span></a>
-<a class="sourceLine" id="cb10-9" data-line-number="9"><span class="co">#> p ~ Visibility</span></a>
-<a class="sourceLine" id="cb10-10" data-line-number="10"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb10-11" data-line-number="11"><span class="co">#> $formula_k</span></a>
-<a class="sourceLine" id="cb10-12" data-line-number="12"><span class="co">#> fixedk </span></a>
-<a class="sourceLine" id="cb10-13" data-line-number="13"><span class="co">#> 0.7 </span></a>
-<a class="sourceLine" id="cb10-14" data-line-number="14"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb10-15" data-line-number="15"><span class="co">#> $predictors</span></a>
-<a class="sourceLine" id="cb10-16" data-line-number="16"><span class="co">#> [1] "Visibility"</span></a>
-<a class="sourceLine" id="cb10-17" data-line-number="17"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb10-18" data-line-number="18"><span class="co">#> $AICc</span></a>
-<a class="sourceLine" id="cb10-19" data-line-number="19"><span class="co">#> [1] 1155.63</span></a>
-<a class="sourceLine" id="cb10-20" data-line-number="20"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb10-21" data-line-number="21"><span class="co">#> $convergence</span></a>
-<a class="sourceLine" id="cb10-22" data-line-number="22"><span class="co">#> [1] 0</span></a>
-<a class="sourceLine" id="cb10-23" data-line-number="23"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb10-24" data-line-number="24"><span class="co">#> $cell_pk</span></a>
-<a class="sourceLine" id="cb10-25" data-line-number="25"><span class="co">#> cell n p_median p_lower p_upper k_median k_lower k_upper</span></a>
-<a class="sourceLine" id="cb10-26" data-line-number="26"><span class="co">#> 1 H 160 0.531 0.474 0.588 0.7 0.7 0.7</span></a>
-<a class="sourceLine" id="cb10-27" data-line-number="27"><span class="co">#> 2 L 160 0.545 0.487 0.601 0.7 0.7 0.7</span></a>
-<a class="sourceLine" id="cb10-28" data-line-number="28"><span class="co">#> 3 M 160 0.538 0.482 0.593 0.7 0.7 0.7</span></a>
-<a class="sourceLine" id="cb10-29" data-line-number="29"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb10-30" data-line-number="30"><span class="co">#> $CL</span></a>
-<a class="sourceLine" id="cb10-31" data-line-number="31"><span class="co">#> [1] 0.9</span></a></code></pre></div>
-</div>
-<div id="set-of-searcher-efficiency-models" class="section level3">
-<h3>Set of Searcher Efficiency Models</h3>
-<p>If the arg <code>allCombos = TRUE</code> is provided, <code>pkm</code> fits a set of <code>pkm</code> models defined as all allowable models simpler than, and including, the provided model for both formulae (where “allowable†means that any interaction terms have all component terms included in the model).</p>
-<p>Consider the following model set analysis, where visibility and habitat type are included in the <span class="math inline">\(p\)</span> formula but only habitat type is in the <span class="math inline">\(k\)</span> formula. This generates a set of 10 models:</p>
-<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" data-line-number="1">pkmModSet <-<span class="st"> </span><span class="kw">pkm</span>(<span class="dt">formula_p =</span> p <span class="op">~</span><span class="st"> </span>Visibility<span class="op">*</span>HabitatType,</a>
-<a class="sourceLine" id="cb11-2" data-line-number="2"> <span class="dt">formula_k =</span> k <span class="op">~</span><span class="st"> </span>HabitatType, <span class="dt">data =</span> data_SE,</a>
-<a class="sourceLine" id="cb11-3" data-line-number="3"> <span class="dt">obsCol =</span> <span class="kw">c</span>(<span class="st">"Search1"</span>, <span class="st">"Search2"</span>, <span class="st">"Search3"</span>, <span class="st">"Search4"</span>),</a>
-<a class="sourceLine" id="cb11-4" data-line-number="4"> <span class="dt">allCombos =</span> <span class="ot">TRUE</span></a>
-<a class="sourceLine" id="cb11-5" data-line-number="5"> )</a>
-<a class="sourceLine" id="cb11-6" data-line-number="6"><span class="kw">class</span>(pkmModSet)</a>
-<a class="sourceLine" id="cb11-7" data-line-number="7"><span class="co">#> [1] "pkmSet" "list"</span></a>
-<a class="sourceLine" id="cb11-8" data-line-number="8"><span class="kw">names</span>(pkmModSet)</a>
-<a class="sourceLine" id="cb11-9" data-line-number="9"><span class="co">#> [1] "p ~ Visibility * HabitatType; k ~ HabitatType"</span></a>
-<a class="sourceLine" id="cb11-10" data-line-number="10"><span class="co">#> [2] "p ~ Visibility + HabitatType; k ~ HabitatType"</span></a>
-<a class="sourceLine" id="cb11-11" data-line-number="11"><span class="co">#> [3] "p ~ HabitatType; k ~ HabitatType" </span></a>
-<a class="sourceLine" id="cb11-12" data-line-number="12"><span class="co">#> [4] "p ~ Visibility; k ~ HabitatType" </span></a>
-<a class="sourceLine" id="cb11-13" data-line-number="13"><span class="co">#> [5] "p ~ 1; k ~ HabitatType" </span></a>
-<a class="sourceLine" id="cb11-14" data-line-number="14"><span class="co">#> [6] "p ~ Visibility * HabitatType; k ~ 1" </span></a>
-<a class="sourceLine" id="cb11-15" data-line-number="15"><span class="co">#> [7] "p ~ Visibility + HabitatType; k ~ 1" </span></a>
-<a class="sourceLine" id="cb11-16" data-line-number="16"><span class="co">#> [8] "p ~ HabitatType; k ~ 1" </span></a>
-<a class="sourceLine" id="cb11-17" data-line-number="17"><span class="co">#> [9] "p ~ Visibility; k ~ 1" </span></a>
-<a class="sourceLine" id="cb11-18" data-line-number="18"><span class="co">#> [10] "p ~ 1; k ~ 1"</span></a></code></pre></div>
-<p>The <code>plot</code> function is defined for the <code>pkmSet</code> class, and by default, creates a new plot window on command for each sub-model. If we want to only plot a specific single (or subset) of models from the full set, we can utilize the <code>specificModel</code> argument:</p>
-<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" data-line-number="1"><span class="kw">plot</span>(pkmModSet, <span class="dt">specificModel =</span> <span class="st">"p ~ Visibility + HabitatType; k ~ 1"</span>)</a></code></pre></div>
-<p>The resulting model outputs can be compared in an AICc table</p>
-<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" data-line-number="1"><span class="kw">aicc</span>(pkmModSet)</a>
-<a class="sourceLine" id="cb13-2" data-line-number="2"><span class="co">#> p Formula k Formula AICc <U+0394>AICc</span></a>
-<a class="sourceLine" id="cb13-3" data-line-number="3"><span class="co">#> 10 p ~ 1 k ~ 1 1145.00 0.00</span></a>
-<a class="sourceLine" id="cb13-4" data-line-number="4"><span class="co">#> 5 p ~ 1 k ~ HabitatType 1145.70 0.70</span></a>
-<a class="sourceLine" id="cb13-5" data-line-number="5"><span class="co">#> 3 p ~ HabitatType k ~ HabitatType 1146.57 1.57</span></a>
-<a class="sourceLine" id="cb13-6" data-line-number="6"><span class="co">#> 8 p ~ HabitatType k ~ 1 1146.76 1.76</span></a>
-<a class="sourceLine" id="cb13-7" data-line-number="7"><span class="co">#> 9 p ~ Visibility k ~ 1 1148.96 3.96</span></a>
-<a class="sourceLine" id="cb13-8" data-line-number="8"><span class="co">#> 4 p ~ Visibility k ~ HabitatType 1149.67 4.67</span></a>
-<a class="sourceLine" id="cb13-9" data-line-number="9"><span class="co">#> 2 p ~ Visibility + HabitatType k ~ HabitatType 1150.55 5.55</span></a>
-<a class="sourceLine" id="cb13-10" data-line-number="10"><span class="co">#> 7 p ~ Visibility + HabitatType k ~ 1 1150.73 5.73</span></a>
-<a class="sourceLine" id="cb13-11" data-line-number="11"><span class="co">#> 1 p ~ Visibility * HabitatType k ~ HabitatType 1153.45 8.45</span></a>
-<a class="sourceLine" id="cb13-12" data-line-number="12"><span class="co">#> 6 p ~ Visibility * HabitatType k ~ 1 1153.49 8.49</span></a></code></pre></div>
-</div>
-<div id="multiple-sizes-of-animals-and-sets-of-searcher-efficiency-models" class="section level3">
-<h3>Multiple Sizes of Animals and Sets of Searcher Efficiency Models</h3>
-<p>Often, carcasses are grouped in multiple size classes, and we are interested in analyzing a set of models separately for each size class. To do so, we use the <code>sizeCol</code> arg to tell <code>pkm</code> which column in <code>data_CP</code> gives the carcass size class. If, in addition, <code>allCombos = TRUE</code>, <code>pkm</code> will fit a <code>pkmSet</code> that runs for each unique size class in the column identified by the <code>sizeCol</code> argument:</p>
-<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb14-1" data-line-number="1">pkmModSetSize <-<span class="st"> </span><span class="kw">pkm</span>(<span class="dt">formula_p =</span> p <span class="op">~</span><span class="st"> </span>Visibility<span class="op">*</span>HabitatType,</a>
-<a class="sourceLine" id="cb14-2" data-line-number="2"> <span class="dt">formula_k =</span> k <span class="op">~</span><span class="st"> </span>HabitatType, <span class="dt">data =</span> data_SE,</a>
-<a class="sourceLine" id="cb14-3" data-line-number="3"> <span class="dt">obsCol =</span> <span class="kw">c</span>(<span class="st">"Search1"</span>, <span class="st">"Search2"</span>, <span class="st">"Search3"</span>, <span class="st">"Search4"</span>),</a>
-<a class="sourceLine" id="cb14-4" data-line-number="4"> <span class="dt">sizeCol =</span> <span class="st">"Size"</span>, <span class="dt">allCombos =</span> <span class="ot">TRUE</span>)</a>
-<a class="sourceLine" id="cb14-5" data-line-number="5"><span class="kw">class</span>(pkmModSetSize)</a>
-<a class="sourceLine" id="cb14-6" data-line-number="6"><span class="co">#> [1] "pkmSetSize" "list"</span></a></code></pre></div>
-<p>The <code>pkmSetSize</code> object is a list where each element corresponds to a different unique size class, and contains the associated <code>pkmSet</code>object, which itself is a list of <code>pkm</code> outputs:</p>
-<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb15-1" data-line-number="1"><span class="kw">names</span>(pkmModSetSize)</a>
-<a class="sourceLine" id="cb15-2" data-line-number="2"><span class="co">#> [1] "L" "M" "S" "XL"</span></a>
-<a class="sourceLine" id="cb15-3" data-line-number="3"><span class="kw">names</span>(pkmModSetSize[[<span class="dv">1</span>]])</a>
-<a class="sourceLine" id="cb15-4" data-line-number="4"><span class="co">#> [1] "p ~ Visibility * HabitatType; k ~ HabitatType"</span></a>
-<a class="sourceLine" id="cb15-5" data-line-number="5"><span class="co">#> [2] "p ~ Visibility + HabitatType; k ~ HabitatType"</span></a>
-<a class="sourceLine" id="cb15-6" data-line-number="6"><span class="co">#> [3] "p ~ HabitatType; k ~ HabitatType" </span></a>
-<a class="sourceLine" id="cb15-7" data-line-number="7"><span class="co">#> [4] "p ~ Visibility; k ~ HabitatType" </span></a>
-<a class="sourceLine" id="cb15-8" data-line-number="8"><span class="co">#> [5] "p ~ 1; k ~ HabitatType" </span></a>
-<a class="sourceLine" id="cb15-9" data-line-number="9"><span class="co">#> [6] "p ~ Visibility * HabitatType; k ~ 1" </span></a>
-<a class="sourceLine" id="cb15-10" data-line-number="10"><span class="co">#> [7] "p ~ Visibility + HabitatType; k ~ 1" </span></a>
-<a class="sourceLine" id="cb15-11" data-line-number="11"><span class="co">#> [8] "p ~ HabitatType; k ~ 1" </span></a>
-<a class="sourceLine" id="cb15-12" data-line-number="12"><span class="co">#> [9] "p ~ Visibility; k ~ 1" </span></a>
-<a class="sourceLine" id="cb15-13" data-line-number="13"><span class="co">#> [10] "p ~ 1; k ~ 1"</span></a></code></pre></div>
-</div>
-</div>
-<div id="carcass-persistence" class="section level2">
-<h2>Carcass Persistence</h2>
-<div id="single-carcass-persistence-model" class="section level3">
-<h3>Single Carcass Persistence Model</h3>
-<p>The central function for carcass persistence analyses is <code>cpm</code>, which, in its simplest form, conducts a singular carcass persistence analysis (<em>i.e.</em>, a singular set of <span class="math inline">\(l\)</span> and <span class="math inline">\(s\)</span> formulae and a singular size classification of carcasses). Note that we use <span class="math inline">\(l\)</span> and <span class="math inline">\(s\)</span> to reference <span class="math inline">\(location\)</span> and <span class="math inline">\(scale\)</span> as the parameters for survival models, following <code>survreg</code>, however we also provide an alternative parameterization (using parameters <span class="math inline">\(a\)</span> and <span class="math inline">\(b\)</span>, referred to as “<code>ab</code>†or “ppersistâ€). As a first example, we will ignore the size category, use intercept-only models for both <span class="math inline">\(l\)</span> and <span class="math inline">\(s\)</span>, and use the Weibull distribution:</p>
-<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb16-1" data-line-number="1">data_CP <-<span class="st"> </span>mock<span class="op">$</span>CP</a>
-<a class="sourceLine" id="cb16-2" data-line-number="2">cpModel <-<span class="st"> </span><span class="kw">cpm</span>(<span class="dt">formula_l =</span> l <span class="op">~</span><span class="st"> </span><span class="dv">1</span>, <span class="dt">formula_s =</span> s <span class="op">~</span><span class="st"> </span><span class="dv">1</span>, <span class="dt">data =</span> data_CP,</a>
-<a class="sourceLine" id="cb16-3" data-line-number="3"> <span class="dt">left =</span> <span class="st">"LastPresentDecimalDays"</span>,</a>
-<a class="sourceLine" id="cb16-4" data-line-number="4"> <span class="dt">right =</span> <span class="st">"FirstAbsentDecimalDays"</span>, <span class="dt">dist =</span> <span class="st">"weibull"</span></a>
-<a class="sourceLine" id="cb16-5" data-line-number="5"> )</a></code></pre></div>
-<p>If successfully fit, a <code>cpm</code> model output contains a number of elements, some printed automatically:</p>
-<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb17-1" data-line-number="1">cpModel</a>
-<a class="sourceLine" id="cb17-2" data-line-number="2"><span class="co">#> $call</span></a>
-<a class="sourceLine" id="cb17-3" data-line-number="3"><span class="co">#> cpm0(formula_l = formula_l, formula_s = formula_s, data = data, </span></a>
-<a class="sourceLine" id="cb17-4" data-line-number="4"><span class="co">#> left = left, right = right, dist = dist, CL = CL, quiet = quiet)</span></a>
-<a class="sourceLine" id="cb17-5" data-line-number="5"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb17-6" data-line-number="6"><span class="co">#> $formula_l</span></a>
-<a class="sourceLine" id="cb17-7" data-line-number="7"><span class="co">#> l ~ 1</span></a>
-<a class="sourceLine" id="cb17-8" data-line-number="8"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb17-9" data-line-number="9"><span class="co">#> $formula_s</span></a>
-<a class="sourceLine" id="cb17-10" data-line-number="10"><span class="co">#> s ~ 1</span></a>
-<a class="sourceLine" id="cb17-11" data-line-number="11"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb17-12" data-line-number="12"><span class="co">#> $distribution</span></a>
-<a class="sourceLine" id="cb17-13" data-line-number="13"><span class="co">#> [1] "weibull"</span></a>
-<a class="sourceLine" id="cb17-14" data-line-number="14"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb17-15" data-line-number="15"><span class="co">#> $predictors</span></a>
-<a class="sourceLine" id="cb17-16" data-line-number="16"><span class="co">#> character(0)</span></a>
-<a class="sourceLine" id="cb17-17" data-line-number="17"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb17-18" data-line-number="18"><span class="co">#> $AICc</span></a>
-<a class="sourceLine" id="cb17-19" data-line-number="19"><span class="co">#> [1] 2102.11</span></a>
-<a class="sourceLine" id="cb17-20" data-line-number="20"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb17-21" data-line-number="21"><span class="co">#> $convergence</span></a>
-<a class="sourceLine" id="cb17-22" data-line-number="22"><span class="co">#> [1] 0</span></a>
-<a class="sourceLine" id="cb17-23" data-line-number="23"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb17-24" data-line-number="24"><span class="co">#> $cell_ls</span></a>
-<a class="sourceLine" id="cb17-25" data-line-number="25"><span class="co">#> cell n l_median l_lower l_upper s_median s_lower s_upper</span></a>
-<a class="sourceLine" id="cb17-26" data-line-number="26"><span class="co">#> 1 all 480 2.671 2.592 2.749 0.966 0.901 1.037</span></a>
-<a class="sourceLine" id="cb17-27" data-line-number="27"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb17-28" data-line-number="28"><span class="co">#> $cell_ab</span></a>
-<a class="sourceLine" id="cb17-29" data-line-number="29"><span class="co">#> cell n pda_median pda_lower pda_upper pdb_median pdb_lower pdb_upper</span></a>
-<a class="sourceLine" id="cb17-30" data-line-number="30"><span class="co">#> 1 all 480 1.035 1.11 0.964 14.454 13.356 15.627</span></a>
-<a class="sourceLine" id="cb17-31" data-line-number="31"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb17-32" data-line-number="32"><span class="co">#> $CL</span></a>
-<a class="sourceLine" id="cb17-33" data-line-number="33"><span class="co">#> [1] 0.9</span></a>
-<a class="sourceLine" id="cb17-34" data-line-number="34"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb17-35" data-line-number="35"><span class="co">#> $cell_desc</span></a>
-<a class="sourceLine" id="cb17-36" data-line-number="36"><span class="co">#> cell medianCP r1 r3 r7 r14 r28</span></a>
-<a class="sourceLine" id="cb17-37" data-line-number="37"><span class="co">#> 1 all 10.1437 0.9696732 0.9094574 0.8003887 0.6463498 0.4427757</span></a></code></pre></div>
-<p>and others available upon request:</p>
-<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb18-1" data-line-number="1"><span class="kw">names</span>(cpModel)</a>
-<a class="sourceLine" id="cb18-2" data-line-number="2"><span class="co">#> [1] "call" "data" "formula_l" "formula_s" </span></a>
-<a class="sourceLine" id="cb18-3" data-line-number="3"><span class="co">#> [5] "distribution" "predictors" "predictors_l" "predictors_s"</span></a>
-<a class="sourceLine" id="cb18-4" data-line-number="4"><span class="co">#> [9] "AIC" "AICc" "convergence" "varbeta" </span></a>
-<a class="sourceLine" id="cb18-5" data-line-number="5"><span class="co">#> [13] "cellMM_l" "cellMM_s" "nbeta_l" "nbeta_s" </span></a>
-<a class="sourceLine" id="cb18-6" data-line-number="6"><span class="co">#> [17] "betahat_l" "betahat_s" "cells" "ncell" </span></a>
-<a class="sourceLine" id="cb18-7" data-line-number="7"><span class="co">#> [21] "cell_ls" "cell_ab" "CL" "observations"</span></a>
-<a class="sourceLine" id="cb18-8" data-line-number="8"><span class="co">#> [25] "carcCells" "loglik" "cell_desc"</span></a>
-<a class="sourceLine" id="cb18-9" data-line-number="9">cpModel<span class="op">$</span>cells</a>
-<a class="sourceLine" id="cb18-10" data-line-number="10"><span class="co">#> group CellNames</span></a>
-<a class="sourceLine" id="cb18-11" data-line-number="11"><span class="co">#> 1 all all</span></a></code></pre></div>
-<p>The <code>plot</code> function has been defined for <code>cpm</code> objects, such that one can simply run</p>
-<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb19-1" data-line-number="1"><span class="kw">plot</span>(cpModel)</a></code></pre></div>
-<p><img 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" style="display: block; margin: auto;" /></p>
-<p>to visualize the model’s output.</p>
-<p>You can generate random draws of the <span class="math inline">\(l\)</span> and <span class="math inline">\(s\)</span> (or <span class="math inline">\(a\)</span> and <span class="math inline">\(b\)</span>) parameters for each cell grouping (in <code>cpModel</code> there are no predictors, so there is one cell grouping called “allâ€) using the <code>rcp</code> function which, like other <code>r*</code> functions in <strong>R</strong> (<em>e.g.</em>, <code>rnorm</code>) takes the number of random draws (<code>n</code>) as the first argument:</p>
-<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb20-1" data-line-number="1"><span class="kw">rcp</span>(<span class="dt">n =</span> <span class="dv">10</span>, cpModel)</a>
-<a class="sourceLine" id="cb20-2" data-line-number="2"><span class="co">#> $all</span></a>
-<a class="sourceLine" id="cb20-3" data-line-number="3"><span class="co">#> l s</span></a>
-<a class="sourceLine" id="cb20-4" data-line-number="4"><span class="co">#> [1,] 2.667938 1.0338505</span></a>
-<a class="sourceLine" id="cb20-5" data-line-number="5"><span class="co">#> [2,] 2.669906 0.9432373</span></a>
-<a class="sourceLine" id="cb20-6" data-line-number="6"><span class="co">#> [3,] 2.624413 0.9426336</span></a>
-<a class="sourceLine" id="cb20-7" data-line-number="7"><span class="co">#> [4,] 2.725055 0.9765017</span></a>
-<a class="sourceLine" id="cb20-8" data-line-number="8"><span class="co">#> [5,] 2.678181 0.9136847</span></a>
-<a class="sourceLine" id="cb20-9" data-line-number="9"><span class="co">#> [6,] 2.614384 0.9599418</span></a>
-<a class="sourceLine" id="cb20-10" data-line-number="10"><span class="co">#> [7,] 2.818511 0.9516491</span></a>
-<a class="sourceLine" id="cb20-11" data-line-number="11"><span class="co">#> [8,] 2.637658 0.9607057</span></a>
-<a class="sourceLine" id="cb20-12" data-line-number="12"><span class="co">#> [9,] 2.725777 0.8937671</span></a>
-<a class="sourceLine" id="cb20-13" data-line-number="13"><span class="co">#> [10,] 2.696035 0.9226017</span></a>
-<a class="sourceLine" id="cb20-14" data-line-number="14"><span class="kw">rcp</span>(<span class="dt">n =</span> <span class="dv">10</span>, cpModel, <span class="dt">type =</span> <span class="st">"ppersist"</span>)</a>
-<a class="sourceLine" id="cb20-15" data-line-number="15"><span class="co">#> $all</span></a>
-<a class="sourceLine" id="cb20-16" data-line-number="16"><span class="co">#> pda pdb</span></a>
-<a class="sourceLine" id="cb20-17" data-line-number="17"><span class="co">#> [1,] 1.0299455 14.22095</span></a>
-<a class="sourceLine" id="cb20-18" data-line-number="18"><span class="co">#> [2,] 1.0631111 15.61477</span></a>
-<a class="sourceLine" id="cb20-19" data-line-number="19"><span class="co">#> [3,] 1.0442141 14.56081</span></a>
-<a class="sourceLine" id="cb20-20" data-line-number="20"><span class="co">#> [4,] 0.9707085 13.79265</span></a>
-<a class="sourceLine" id="cb20-21" data-line-number="21"><span class="co">#> [5,] 1.0665873 15.08344</span></a>
-<a class="sourceLine" id="cb20-22" data-line-number="22"><span class="co">#> [6,] 1.0414856 14.00758</span></a>
-<a class="sourceLine" id="cb20-23" data-line-number="23"><span class="co">#> [7,] 1.0420301 14.15536</span></a>
-<a class="sourceLine" id="cb20-24" data-line-number="24"><span class="co">#> [8,] 1.0371340 13.97719</span></a>
-<a class="sourceLine" id="cb20-25" data-line-number="25"><span class="co">#> [9,] 1.0554227 15.74361</span></a>
-<a class="sourceLine" id="cb20-26" data-line-number="26"><span class="co">#> [10,] 1.0344494 13.84287</span></a></code></pre></div>
-<p>You can complicate the <span class="math inline">\(l\)</span> and <span class="math inline">\(s\)</span> formulae independently</p>
-<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb21-1" data-line-number="1"><span class="kw">cpm</span>(<span class="dt">formula_l =</span> l <span class="op">~</span><span class="st"> </span>Visibility <span class="op">*</span><span class="st"> </span>GroundCover, <span class="dt">formula_s =</span> s <span class="op">~</span><span class="st"> </span><span class="dv">1</span>, <span class="dt">data =</span> data_CP,</a>
-<a class="sourceLine" id="cb21-2" data-line-number="2"> <span class="dt">left =</span> <span class="st">"LastPresentDecimalDays"</span>, <span class="dt">right =</span> <span class="st">"FirstAbsentDecimalDays"</span>,</a>
-<a class="sourceLine" id="cb21-3" data-line-number="3"> <span class="dt">dist =</span> <span class="st">"weibull"</span></a>
-<a class="sourceLine" id="cb21-4" data-line-number="4">)</a>
-<a class="sourceLine" id="cb21-5" data-line-number="5"><span class="co">#> $call</span></a>
-<a class="sourceLine" id="cb21-6" data-line-number="6"><span class="co">#> cpm0(formula_l = formula_l, formula_s = formula_s, data = data, </span></a>
-<a class="sourceLine" id="cb21-7" data-line-number="7"><span class="co">#> left = left, right = right, dist = dist, CL = CL, quiet = quiet)</span></a>
-<a class="sourceLine" id="cb21-8" data-line-number="8"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb21-9" data-line-number="9"><span class="co">#> $formula_l</span></a>
-<a class="sourceLine" id="cb21-10" data-line-number="10"><span class="co">#> l ~ Visibility * GroundCover</span></a>
-<a class="sourceLine" id="cb21-11" data-line-number="11"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb21-12" data-line-number="12"><span class="co">#> $formula_s</span></a>
-<a class="sourceLine" id="cb21-13" data-line-number="13"><span class="co">#> s ~ 1</span></a>
-<a class="sourceLine" id="cb21-14" data-line-number="14"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb21-15" data-line-number="15"><span class="co">#> $distribution</span></a>
-<a class="sourceLine" id="cb21-16" data-line-number="16"><span class="co">#> [1] "weibull"</span></a>
-<a class="sourceLine" id="cb21-17" data-line-number="17"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb21-18" data-line-number="18"><span class="co">#> $predictors</span></a>
-<a class="sourceLine" id="cb21-19" data-line-number="19"><span class="co">#> [1] "Visibility" "GroundCover"</span></a>
-<a class="sourceLine" id="cb21-20" data-line-number="20"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb21-21" data-line-number="21"><span class="co">#> $AICc</span></a>
-<a class="sourceLine" id="cb21-22" data-line-number="22"><span class="co">#> [1] 2109.36</span></a>
-<a class="sourceLine" id="cb21-23" data-line-number="23"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb21-24" data-line-number="24"><span class="co">#> $convergence</span></a>
-<a class="sourceLine" id="cb21-25" data-line-number="25"><span class="co">#> [1] 0</span></a>
-<a class="sourceLine" id="cb21-26" data-line-number="26"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb21-27" data-line-number="27"><span class="co">#> $cell_ls</span></a>
-<a class="sourceLine" id="cb21-28" data-line-number="28"><span class="co">#> cell n l_median l_lower l_upper s_median s_lower s_upper</span></a>
-<a class="sourceLine" id="cb21-29" data-line-number="29"><span class="co">#> 1 H.A 80 2.647 2.457 2.836 0.964 0.898 1.034</span></a>
-<a class="sourceLine" id="cb21-30" data-line-number="30"><span class="co">#> 2 L.A 80 2.618 2.427 2.809 0.964 0.898 1.034</span></a>
-<a class="sourceLine" id="cb21-31" data-line-number="31"><span class="co">#> 3 M.A 80 2.536 2.350 2.722 0.964 0.898 1.034</span></a>
-<a class="sourceLine" id="cb21-32" data-line-number="32"><span class="co">#> 4 H.B 80 2.789 2.597 2.981 0.964 0.898 1.034</span></a>
-<a class="sourceLine" id="cb21-33" data-line-number="33"><span class="co">#> 5 L.B 80 2.710 2.521 2.900 0.964 0.898 1.034</span></a>
-<a class="sourceLine" id="cb21-34" data-line-number="34"><span class="co">#> 6 M.B 80 2.716 2.527 2.905 0.964 0.898 1.034</span></a>
-<a class="sourceLine" id="cb21-35" data-line-number="35"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb21-36" data-line-number="36"><span class="co">#> $cell_ab</span></a>
-<a class="sourceLine" id="cb21-37" data-line-number="37"><span class="co">#> cell n pda_median pda_lower pda_upper pdb_median pdb_lower pdb_upper</span></a>
-<a class="sourceLine" id="cb21-38" data-line-number="38"><span class="co">#> 1 H.A 80 1.037 1.114 0.967 14.112 11.670 17.047</span></a>
-<a class="sourceLine" id="cb21-39" data-line-number="39"><span class="co">#> 2 L.A 80 1.037 1.114 0.967 13.708 11.325 16.593</span></a>
-<a class="sourceLine" id="cb21-40" data-line-number="40"><span class="co">#> 3 M.A 80 1.037 1.114 0.967 12.629 10.486 15.211</span></a>
-<a class="sourceLine" id="cb21-41" data-line-number="41"><span class="co">#> 4 H.B 80 1.037 1.114 0.967 16.265 13.423 19.708</span></a>
-<a class="sourceLine" id="cb21-42" data-line-number="42"><span class="co">#> 5 L.B 80 1.037 1.114 0.967 15.029 12.441 18.174</span></a>
-<a class="sourceLine" id="cb21-43" data-line-number="43"><span class="co">#> 6 M.B 80 1.037 1.114 0.967 15.120 12.516 18.265</span></a>
-<a class="sourceLine" id="cb21-44" data-line-number="44"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb21-45" data-line-number="45"><span class="co">#> $CL</span></a>
-<a class="sourceLine" id="cb21-46" data-line-number="46"><span class="co">#> [1] 0.9</span></a>
-<a class="sourceLine" id="cb21-47" data-line-number="47"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb21-48" data-line-number="48"><span class="co">#> $cell_desc</span></a>
-<a class="sourceLine" id="cb21-49" data-line-number="49"><span class="co">#> cell medianCP r1 r3 r7 r14 r28</span></a>
-<a class="sourceLine" id="cb21-50" data-line-number="50"><span class="co">#> 1 H.A 9.910449 0.9691191 0.9076888 0.7965531 0.6402617 0.4355206</span></a>
-<a class="sourceLine" id="cb21-51" data-line-number="51"><span class="co">#> 2 L.A 9.626732 0.9681953 0.9050529 0.7912752 0.6323916 0.4266794</span></a>
-<a class="sourceLine" id="cb21-52" data-line-number="52"><span class="co">#> 3 M.A 8.868981 0.9654396 0.8972323 0.7757825 0.6096922 0.4019134</span></a>
-<a class="sourceLine" id="cb21-53" data-line-number="53"><span class="co">#> 4 H.B 11.422439 0.9732702 0.9196215 0.8208024 0.6773283 0.4789728</span></a>
-<a class="sourceLine" id="cb21-54" data-line-number="54"><span class="co">#> 5 L.B 10.554432 0.9710322 0.9131703 0.8076196 0.6569919 0.4547593</span></a>
-<a class="sourceLine" id="cb21-55" data-line-number="55"><span class="co">#> 6 M.B 10.618339 0.9712095 0.9136797 0.8086542 0.6585719 0.4566077</span></a></code></pre></div>
-<p>Given that the exponential only has one parameter (<span class="math inline">\(l\)</span>, location), a model for scale (<code>formula_s</code>) is not required:</p>
-<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb22-1" data-line-number="1">cpModExp <-<span class="st"> </span><span class="kw">cpm</span>(<span class="dt">formula_l =</span> l <span class="op">~</span><span class="st"> </span>Visibility <span class="op">*</span><span class="st"> </span>GroundCover, <span class="dt">data =</span> data_CP,</a>
-<a class="sourceLine" id="cb22-2" data-line-number="2"> <span class="dt">left =</span> <span class="st">"LastPresentDecimalDays"</span>,</a>
-<a class="sourceLine" id="cb22-3" data-line-number="3"> <span class="dt">right =</span> <span class="st">"FirstAbsentDecimalDays"</span>, <span class="dt">dist =</span> <span class="st">"exponential"</span></a>
-<a class="sourceLine" id="cb22-4" data-line-number="4"> )</a></code></pre></div>
-</div>
-<div id="set-of-carcass-persistence-models" class="section level3">
-<h3>Set of Carcass Persistence Models</h3>
-<p>If the arg <code>allCombos = TRUE</code> is provided, <code>cpm</code> fits a set of <code>cpm</code> models defined as all allowable models simpler than, and including, the provided model formulae (where “allowable†means that any interaction terms have all component terms included in the model).</p>
-<p>In addition, <code>cpm</code> with <code>allCombos</code> can include any subset of the four base distributions (exponential, weibull, lognormal, loglogistic) and crosses them with the predictor models.</p>
-<p>Consider the following model set analysis, where <code>Visibility</code> and <code>Season</code> are included in the <span class="math inline">\(l\)</span> formula but only <code>Visibility</code> is in the <span class="math inline">\(s\)</span> formula, and only the exponential and lognormal distributions are included. This generates a set of 15 models:</p>
-<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb23-1" data-line-number="1">cpmModSet <-<span class="st"> </span><span class="kw">cpm</span>(<span class="dt">formula_l =</span> l <span class="op">~</span><span class="st"> </span>Visibility <span class="op">*</span><span class="st"> </span>Season,</a>
-<a class="sourceLine" id="cb23-2" data-line-number="2"> <span class="dt">formula_s =</span> s <span class="op">~</span><span class="st"> </span>Visibility, <span class="dt">data =</span> data_CP,</a>
-<a class="sourceLine" id="cb23-3" data-line-number="3"> <span class="dt">left =</span> <span class="st">"LastPresentDecimalDays"</span>,</a>
-<a class="sourceLine" id="cb23-4" data-line-number="4"> <span class="dt">right =</span> <span class="st">"FirstAbsentDecimalDays"</span>,</a>
-<a class="sourceLine" id="cb23-5" data-line-number="5"> <span class="dt">dist =</span> <span class="kw">c</span>(<span class="st">"exponential"</span>, <span class="st">"lognormal"</span>), <span class="dt">allCombos =</span> <span class="ot">TRUE</span></a>
-<a class="sourceLine" id="cb23-6" data-line-number="6"> )</a>
-<a class="sourceLine" id="cb23-7" data-line-number="7"><span class="kw">class</span>(cpmModSet)</a>
-<a class="sourceLine" id="cb23-8" data-line-number="8"><span class="co">#> [1] "cpmSet" "list"</span></a>
-<a class="sourceLine" id="cb23-9" data-line-number="9"><span class="kw">names</span>(cpmModSet)</a>
-<a class="sourceLine" id="cb23-10" data-line-number="10"><span class="co">#> [1] "dist: exponential; l ~ Visibility * Season; NULL" </span></a>
-<a class="sourceLine" id="cb23-11" data-line-number="11"><span class="co">#> [2] "dist: exponential; l ~ Visibility + Season; NULL" </span></a>
-<a class="sourceLine" id="cb23-12" data-line-number="12"><span class="co">#> [3] "dist: exponential; l ~ Season; NULL" </span></a>
-<a class="sourceLine" id="cb23-13" data-line-number="13"><span class="co">#> [4] "dist: exponential; l ~ Visibility; NULL" </span></a>
-<a class="sourceLine" id="cb23-14" data-line-number="14"><span class="co">#> [5] "dist: exponential; l ~ 1; NULL" </span></a>
-<a class="sourceLine" id="cb23-15" data-line-number="15"><span class="co">#> [6] "dist: lognormal; l ~ Visibility * Season; s ~ Visibility"</span></a>
-<a class="sourceLine" id="cb23-16" data-line-number="16"><span class="co">#> [7] "dist: lognormal; l ~ Visibility + Season; s ~ Visibility"</span></a>
-<a class="sourceLine" id="cb23-17" data-line-number="17"><span class="co">#> [8] "dist: lognormal; l ~ Season; s ~ Visibility" </span></a>
-<a class="sourceLine" id="cb23-18" data-line-number="18"><span class="co">#> [9] "dist: lognormal; l ~ Visibility; s ~ Visibility" </span></a>
-<a class="sourceLine" id="cb23-19" data-line-number="19"><span class="co">#> [10] "dist: lognormal; l ~ 1; s ~ Visibility" </span></a>
-<a class="sourceLine" id="cb23-20" data-line-number="20"><span class="co">#> [11] "dist: lognormal; l ~ Visibility * Season; s ~ 1" </span></a>
-<a class="sourceLine" id="cb23-21" data-line-number="21"><span class="co">#> [12] "dist: lognormal; l ~ Visibility + Season; s ~ 1" </span></a>
-<a class="sourceLine" id="cb23-22" data-line-number="22"><span class="co">#> [13] "dist: lognormal; l ~ Season; s ~ 1" </span></a>
-<a class="sourceLine" id="cb23-23" data-line-number="23"><span class="co">#> [14] "dist: lognormal; l ~ Visibility; s ~ 1" </span></a>
-<a class="sourceLine" id="cb23-24" data-line-number="24"><span class="co">#> [15] "dist: lognormal; l ~ 1; s ~ 1"</span></a></code></pre></div>
-<p>The resulting model outputs can be compared in an AICc table</p>
-<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb24-1" data-line-number="1"><span class="kw">aicc</span>(cpmModSet)</a>
-<a class="sourceLine" id="cb24-2" data-line-number="2"><span class="co">#> Distribution Location Formula Scale Formula AICc <U+0394>AICc</span></a>
-<a class="sourceLine" id="cb24-3" data-line-number="3"><span class="co">#> 5 exponential l ~ 1 NULL 2100.72 0.00</span></a>
-<a class="sourceLine" id="cb24-4" data-line-number="4"><span class="co">#> 3 exponential l ~ Season NULL 2101.08 0.36</span></a>
-<a class="sourceLine" id="cb24-5" data-line-number="5"><span class="co">#> 4 exponential l ~ Visibility NULL 2104.16 3.44</span></a>
-<a class="sourceLine" id="cb24-6" data-line-number="6"><span class="co">#> 2 exponential l ~ Visibility + Season NULL 2104.48 3.76</span></a>
-<a class="sourceLine" id="cb24-7" data-line-number="7"><span class="co">#> 1 exponential l ~ Visibility * Season NULL 2108.17 7.45</span></a>
-<a class="sourceLine" id="cb24-8" data-line-number="8"><span class="co">#> 15 lognormal l ~ 1 s ~ 1 2159.24 58.52</span></a>
-<a class="sourceLine" id="cb24-9" data-line-number="9"><span class="co">#> 13 lognormal l ~ Season s ~ 1 2160.78 60.06</span></a>
-<a class="sourceLine" id="cb24-10" data-line-number="10"><span class="co">#> 10 lognormal l ~ 1 s ~ Visibility 2162.15 61.43</span></a>
-<a class="sourceLine" id="cb24-11" data-line-number="11"><span class="co">#> 14 lognormal l ~ Visibility s ~ 1 2163.19 62.47</span></a>
-<a class="sourceLine" id="cb24-12" data-line-number="12"><span class="co">#> 8 lognormal l ~ Season s ~ Visibility 2163.67 62.95</span></a>
-<a class="sourceLine" id="cb24-13" data-line-number="13"><span class="co">#> 12 lognormal l ~ Visibility + Season s ~ 1 2164.74 64.02</span></a>
-<a class="sourceLine" id="cb24-14" data-line-number="14"><span class="co">#> 9 lognormal l ~ Visibility s ~ Visibility 2166.13 65.41</span></a>
-<a class="sourceLine" id="cb24-15" data-line-number="15"><span class="co">#> 11 lognormal l ~ Visibility * Season s ~ 1 2166.91 66.19</span></a>
-<a class="sourceLine" id="cb24-16" data-line-number="16"><span class="co">#> 7 lognormal l ~ Visibility + Season s ~ Visibility 2167.66 66.94</span></a>
-<a class="sourceLine" id="cb24-17" data-line-number="17"><span class="co">#> 6 lognormal l ~ Visibility * Season s ~ Visibility 2169.79 69.07</span></a></code></pre></div>
-<p>The <code>plot</code> function is defined for the <code>cpmSet</code> class, and by default, creates a new plot window on command for each sub-model. If we want to only plot a specific single (or subset) of models from the full set, we can utilize the <code>specificModel</code> argument:</p>
-<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb25-1" data-line-number="1"><span class="kw">plot</span>(cpmModSet,</a>
-<a class="sourceLine" id="cb25-2" data-line-number="2"> <span class="dt">specificModel =</span> <span class="st">"dist: lognormal; l ~ Visibility * Season; s ~ Visibility"</span></a>
-<a class="sourceLine" id="cb25-3" data-line-number="3">)</a></code></pre></div>
-<p><img 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" style="display: block; margin: auto;" /></p>
-</div>
-<div id="multiple-sizes-of-animals-and-sets-of-carcass-persistence-models" class="section level3">
-<h3>Multiple Sizes of Animals and Sets of Carcass Persistence Models</h3>
-<p>Often, carcasses are grouped in multiple size classes, and we are interested in analyzing a set of models separately for each size class. To do so, we furnish <code>cpm</code> with <code>sizeCol</code>, which is the name of the column in <code>data_CP</code> that gives the size classes of the carcasses. If, in addition, <code>allCombos = TRUE</code>, then <code>cpm</code> returns a <code>cpmSet</code> for each unique size class in the column identified by the <code>sizeCol</code> argument:</p>
-<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb26-1" data-line-number="1">cpmModSetSize <-<span class="st"> </span><span class="kw">cpm</span>(<span class="dt">formula_l =</span> l <span class="op">~</span><span class="st"> </span>Visibility <span class="op">*</span><span class="st"> </span>Season,</a>
-<a class="sourceLine" id="cb26-2" data-line-number="2"> <span class="dt">formula_s =</span> s <span class="op">~</span><span class="st"> </span>Visibility, <span class="dt">data =</span> data_CP,</a>
-<a class="sourceLine" id="cb26-3" data-line-number="3"> <span class="dt">left =</span> <span class="st">"LastPresentDecimalDays"</span>,</a>
-<a class="sourceLine" id="cb26-4" data-line-number="4"> <span class="dt">right =</span> <span class="st">"FirstAbsentDecimalDays"</span>,</a>
-<a class="sourceLine" id="cb26-5" data-line-number="5"> <span class="dt">dist =</span> <span class="kw">c</span>(<span class="st">"exponential"</span>, <span class="st">"lognormal"</span>),</a>
-<a class="sourceLine" id="cb26-6" data-line-number="6"> <span class="dt">sizeCol =</span> <span class="st">"Size"</span>, <span class="dt">allCombos =</span> <span class="ot">TRUE</span>)</a>
-<a class="sourceLine" id="cb26-7" data-line-number="7"><span class="kw">class</span>(cpmModSetSize)</a>
-<a class="sourceLine" id="cb26-8" data-line-number="8"><span class="co">#> [1] "cpmSetSize" "list"</span></a></code></pre></div>
-<p>The <code>cpmSetSize</code> object is a list where each element corresponds to a different unique size class, and contains the associated <code>cpmSet</code>o bject, which itself is a list of <code>cpm</code> outputs:</p>
-<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb27-1" data-line-number="1"><span class="kw">names</span>(cpmModSetSize)</a>
-<a class="sourceLine" id="cb27-2" data-line-number="2"><span class="co">#> [1] "L" "M" "S" "XL"</span></a>
-<a class="sourceLine" id="cb27-3" data-line-number="3"><span class="kw">names</span>(cpmModSetSize[[<span class="dv">1</span>]])</a>
-<a class="sourceLine" id="cb27-4" data-line-number="4"><span class="co">#> [1] "dist: exponential; l ~ Visibility * Season; NULL" </span></a>
-<a class="sourceLine" id="cb27-5" data-line-number="5"><span class="co">#> [2] "dist: exponential; l ~ Visibility + Season; NULL" </span></a>
-<a class="sourceLine" id="cb27-6" data-line-number="6"><span class="co">#> [3] "dist: exponential; l ~ Season; NULL" </span></a>
-<a class="sourceLine" id="cb27-7" data-line-number="7"><span class="co">#> [4] "dist: exponential; l ~ Visibility; NULL" </span></a>
-<a class="sourceLine" id="cb27-8" data-line-number="8"><span class="co">#> [5] "dist: exponential; l ~ 1; NULL" </span></a>
-<a class="sourceLine" id="cb27-9" data-line-number="9"><span class="co">#> [6] "dist: lognormal; l ~ Visibility * Season; s ~ Visibility"</span></a>
-<a class="sourceLine" id="cb27-10" data-line-number="10"><span class="co">#> [7] "dist: lognormal; l ~ Visibility + Season; s ~ Visibility"</span></a>
-<a class="sourceLine" id="cb27-11" data-line-number="11"><span class="co">#> [8] "dist: lognormal; l ~ Season; s ~ Visibility" </span></a>
-<a class="sourceLine" id="cb27-12" data-line-number="12"><span class="co">#> [9] "dist: lognormal; l ~ Visibility; s ~ Visibility" </span></a>
-<a class="sourceLine" id="cb27-13" data-line-number="13"><span class="co">#> [10] "dist: lognormal; l ~ 1; s ~ Visibility" </span></a>
-<a class="sourceLine" id="cb27-14" data-line-number="14"><span class="co">#> [11] "dist: lognormal; l ~ Visibility * Season; s ~ 1" </span></a>
-<a class="sourceLine" id="cb27-15" data-line-number="15"><span class="co">#> [12] "dist: lognormal; l ~ Visibility + Season; s ~ 1" </span></a>
-<a class="sourceLine" id="cb27-16" data-line-number="16"><span class="co">#> [13] "dist: lognormal; l ~ Season; s ~ 1" </span></a>
-<a class="sourceLine" id="cb27-17" data-line-number="17"><span class="co">#> [14] "dist: lognormal; l ~ Visibility; s ~ 1" </span></a>
-<a class="sourceLine" id="cb27-18" data-line-number="18"><span class="co">#> [15] "dist: lognormal; l ~ 1; s ~ 1"</span></a>
-<a class="sourceLine" id="cb27-19" data-line-number="19"><span class="kw">class</span>(cpmModSetSize[[<span class="dv">1</span>]])</a>
-<a class="sourceLine" id="cb27-20" data-line-number="20"><span class="co">#> [1] "cpmSet" "list"</span></a></code></pre></div>
-</div>
-</div>
-<div id="generic-detection-probability" class="section level2">
-<h2>Generic Detection Probability</h2>
-<p>For the purposes of mortality estimation, we calculate carcass-specific detection probabilities (see below), which may be difficult to generalize, given the specific history of each observed carcass. Thus, we also provide a simple means to calculate generic detection probabilities that are cell-specific, rather than carcass-specific.</p>
-<p>For any estimation of detection probability (<span class="math inline">\(\hat{g}\)</span>), we need to have singular SE and CP models to use for each of the size classes. Here, we use the best-fit of the models for each size class:</p>
-<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb28-1" data-line-number="1">pkMods <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"S"</span> =<span class="st"> "p ~ 1; k ~ 1"</span>, <span class="st">"L"</span> =<span class="st"> "p ~ 1; k ~ 1"</span>,</a>
-<a class="sourceLine" id="cb28-2" data-line-number="2"> <span class="st">"M"</span> =<span class="st"> "p ~ 1; k ~ 1"</span>, <span class="st">"XL"</span> =<span class="st"> "p ~ 1; k ~ HabitatType"</span></a>
-<a class="sourceLine" id="cb28-3" data-line-number="3"> )</a>
-<a class="sourceLine" id="cb28-4" data-line-number="4">cpMods <-<span class="st"> </span><span class="kw">c</span>(<span class="st">"S"</span> =<span class="st"> "dist: exponential; l ~ Season; NULL"</span>,</a>
-<a class="sourceLine" id="cb28-5" data-line-number="5"> <span class="st">"L"</span> =<span class="st"> "dist: exponential; l ~ 1; NULL"</span>,</a>
-<a class="sourceLine" id="cb28-6" data-line-number="6"> <span class="st">"M"</span> =<span class="st"> "dist: exponential; l ~ 1; NULL"</span>,</a>
-<a class="sourceLine" id="cb28-7" data-line-number="7"> <span class="st">"XL"</span> =<span class="st"> "dist: exponential; l ~ 1; NULL"</span></a>
-<a class="sourceLine" id="cb28-8" data-line-number="8"> )</a></code></pre></div>
-<p>The <code>estgGenericSize</code> function produces <code>n</code> random draws of generic (i.e., cell-specific, not carcass-sepecific) detection probabilities for each of the possible carcass cell combinations across the selected SE and CP models across the size classes. <code>estgGeneric</code> is a single-size-class version of function and <code>estgGenericSize</code> actually loops over <code>estgGeneric</code>. The generic <span class="math inline">\(\hat{g}\)</span> is estimated according to a particular search schedule. When we pass <code>averageSS</code> a full <code>data_SS</code> table like we have here, it will assume that columns filled exclusively with 0s and 1s represent search schedules for units and will create the average search schedule across the units.</p>
-<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb29-1" data-line-number="1">data_SS <-<span class="st"> </span>mock<span class="op">$</span>SS</a>
-<a class="sourceLine" id="cb29-2" data-line-number="2">avgSS <-<span class="st"> </span><span class="kw">averageSS</span>(data_SS)</a>
-<a class="sourceLine" id="cb29-3" data-line-number="3"></a>
-<a class="sourceLine" id="cb29-4" data-line-number="4">gsGeneric <-<span class="st"> </span><span class="kw">estgGenericSize</span>(<span class="dt">nsim =</span> <span class="dv">1000</span>, <span class="dt">days =</span> avgSS,</a>
-<a class="sourceLine" id="cb29-5" data-line-number="5"> <span class="dt">modelSetSize_SE =</span> pkmModSetSize,</a>
-<a class="sourceLine" id="cb29-6" data-line-number="6"> <span class="dt">modelSetSize_CP =</span> cpmModSetSize,</a>
-<a class="sourceLine" id="cb29-7" data-line-number="7"> <span class="dt">modelSizeSelections_SE =</span> pkMods,</a>
-<a class="sourceLine" id="cb29-8" data-line-number="8"> <span class="dt">modelSizeSelections_CP =</span> cpMods</a>
-<a class="sourceLine" id="cb29-9" data-line-number="9"> )</a></code></pre></div>
-<p>The output from <code>estgGeneric</code> can be simply summarized</p>
-<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb30-1" data-line-number="1"><span class="kw">summary</span>(gsGeneric)</a>
-<a class="sourceLine" id="cb30-2" data-line-number="2"><span class="co">#> $L</span></a>
-<a class="sourceLine" id="cb30-3" data-line-number="3"><span class="co">#> Group 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb30-4" data-line-number="4"><span class="co">#> 1 all 0.376 0.409 0.43 0.452 0.486</span></a>
-<a class="sourceLine" id="cb30-5" data-line-number="5"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb30-6" data-line-number="6"><span class="co">#> $M</span></a>
-<a class="sourceLine" id="cb30-7" data-line-number="7"><span class="co">#> Group 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb30-8" data-line-number="8"><span class="co">#> 1 all 0.351 0.386 0.406 0.429 0.462</span></a>
-<a class="sourceLine" id="cb30-9" data-line-number="9"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb30-10" data-line-number="10"><span class="co">#> $S</span></a>
-<a class="sourceLine" id="cb30-11" data-line-number="11"><span class="co">#> Season 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb30-12" data-line-number="12"><span class="co">#> 1 SF 0.359 0.398 0.426 0.452 0.493</span></a>
-<a class="sourceLine" id="cb30-13" data-line-number="13"><span class="co">#> 2 WS 0.298 0.335 0.358 0.383 0.419</span></a>
-<a class="sourceLine" id="cb30-14" data-line-number="14"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb30-15" data-line-number="15"><span class="co">#> $XL</span></a>
-<a class="sourceLine" id="cb30-16" data-line-number="16"><span class="co">#> HabitatType 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb30-17" data-line-number="17"><span class="co">#> 1 HT1 0.315 0.346 0.369 0.393 0.426</span></a>
-<a class="sourceLine" id="cb30-18" data-line-number="18"><span class="co">#> 2 HT2 0.329 0.359 0.383 0.407 0.438</span></a></code></pre></div>
-<p>or plotted.</p>
-<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb31-1" data-line-number="1"><span class="kw">plot</span>(gsGeneric)</a></code></pre></div>
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" style="display: block; margin: auto;" /></p>
-</div>
-<div id="mortality-estimation" class="section level2">
-<h2>Mortality Estimation</h2>
-<p>When estimating mortality, detection probability is determined for individual carcasses based on the dates when they are observed, size class values, associated covariates, the searcher efficiency and carcass persistence models, and the search schedule. The carcass-specific detection probabilities (as opposed to the generic/cell-specific detection probabilities above) are therefore calculated before estimating the total mortality. Although it is possible to estimate these detection probabilities separately, they are best interpreted in the context of a full mortality estimation.</p>
-<p>The <code>estM</code> function is the general wrapper function for estimating <code>M</code>, whether for a single size class or multiple size classes. Prior to estimation, we need to reduce the model-set-size complexed to just a single chosen model per size class, corresponding to the <code>pkMods</code> and <code>cpMods</code> vectors given above. To reduce the model set complexity, we can use the <code>trimSetSize</code> function:</p>
-<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb32-1" data-line-number="1">pkmModSize <-<span class="st"> </span><span class="kw">trimSetSize</span>(pkmModSetSize, pkMods)</a>
-<a class="sourceLine" id="cb32-2" data-line-number="2">cpmModSize <-<span class="st"> </span><span class="kw">trimSetSize</span>(cpmModSetSize, cpMods)</a></code></pre></div>
-<p>In addition to the models and search schedule data, <code>estM</code> requires density-weighted proportion (DWP) and carcass observation (CO) data. If more than one size class is represented in the data, a required input is also the column names associated with the DWP value for each size class (argument <code>DWPCol</code> in <code>estM</code>):</p>
-<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb33-1" data-line-number="1">data_CO <-<span class="st"> </span>mock<span class="op">$</span>CO</a>
-<a class="sourceLine" id="cb33-2" data-line-number="2">data_DWP <-<span class="st"> </span>mock<span class="op">$</span>DWP</a>
-<a class="sourceLine" id="cb33-3" data-line-number="3"><span class="kw">head</span>(data_DWP)</a>
-<a class="sourceLine" id="cb33-4" data-line-number="4"><span class="co">#> Unit S M L XL</span></a>
-<a class="sourceLine" id="cb33-5" data-line-number="5"><span class="co">#> 1 Unit1 0.70 0.70 0.60 0.60</span></a>
-<a class="sourceLine" id="cb33-6" data-line-number="6"><span class="co">#> 2 Unit2 0.70 0.70 0.60 0.60</span></a>
-<a class="sourceLine" id="cb33-7" data-line-number="7"><span class="co">#> 3 Unit3 0.56 0.56 0.48 0.48</span></a>
-<a class="sourceLine" id="cb33-8" data-line-number="8"><span class="co">#> 4 Unit4 0.56 0.56 0.48 0.48</span></a>
-<a class="sourceLine" id="cb33-9" data-line-number="9"><span class="co">#> 5 Unit5 0.70 0.70 0.60 0.60</span></a>
-<a class="sourceLine" id="cb33-10" data-line-number="10">DWPcolnames <-<span class="st"> </span><span class="kw">names</span>(pkmModSize)</a>
-<a class="sourceLine" id="cb33-11" data-line-number="11"></a>
-<a class="sourceLine" id="cb33-12" data-line-number="12">eM <-<span class="st"> </span><span class="kw">estM</span>(<span class="dt">data_CO =</span> data_CO, <span class="dt">data_SS =</span> data_SS, <span class="dt">data_DWP =</span> data_DWP,</a>
-<a class="sourceLine" id="cb33-13" data-line-number="13"> <span class="dt">frac =</span> <span class="dv">1</span>, <span class="dt">model_SE =</span> pkmModSize, <span class="dt">model_CP =</span> cpmModSize,</a>
-<a class="sourceLine" id="cb33-14" data-line-number="14"> <span class="dt">seed_SE =</span> <span class="ot">NULL</span>, <span class="dt">seed_CP =</span> <span class="ot">NULL</span>, <span class="dt">seed_g =</span> <span class="ot">NULL</span>, <span class="dt">seed_M =</span> <span class="ot">NULL</span>,</a>
-<a class="sourceLine" id="cb33-15" data-line-number="15"> <span class="dt">unitCol =</span> <span class="st">"Unit"</span>, <span class="dt">COdate =</span> <span class="st">"DateFound"</span>,</a>
-<a class="sourceLine" id="cb33-16" data-line-number="16"> <span class="dt">SSdate =</span> <span class="st">"DateSearched"</span>, <span class="dt">sizeCol =</span> <span class="st">"Size"</span>, <span class="dt">nsim =</span> <span class="dv">1000</span>)</a></code></pre></div>
-<p><code>estM</code> returns an object that contains the random draws of <code>pkm</code> and <code>cpm</code> parameters (named <code>pk</code> and <code>ab</code>, respectively) and the estimated carcass-level detection parameters (<code>g</code>), arrival intervals (<code>Aj</code>), and associated total mortality (<code>Mhat</code>) values for each simulation. These <code>Mhat</code> values should be considered in combination, and can be summarized and plotted simply:</p>
-<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb34-1" data-line-number="1"><span class="kw">summary</span>(eM)</a>
-<a class="sourceLine" id="cb34-2" data-line-number="2"><span class="co">#> median 5% 95% </span></a>
-<a class="sourceLine" id="cb34-3" data-line-number="3"><span class="co">#> 1811.09 1647.66 1982.47</span></a>
-<a class="sourceLine" id="cb34-4" data-line-number="4"><span class="kw">plot</span>(eM)</a></code></pre></div>
-<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
-<div id="splitting-mortality-estimations" class="section level3">
-<h3>Splitting Mortality Estimations</h3>
-<p>It is possible to split the resulting mortality estimation into components that are denoted according to covariates in either the search schedule or carcass observation data sets.</p>
-<p>First, a temporal split:</p>
-<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb35-1" data-line-number="1">M_season <-<span class="st"> </span><span class="kw">calcSplits</span>(<span class="dt">M =</span> eM, <span class="dt">split_SS =</span> <span class="st">"Construction"</span>,</a>
-<a class="sourceLine" id="cb35-2" data-line-number="2"> <span class="dt">split_CO =</span> <span class="ot">NULL</span>, <span class="dt">data_SS =</span> data_SS, <span class="dt">data_CO =</span> data_CO</a>
-<a class="sourceLine" id="cb35-3" data-line-number="3"> )</a>
-<a class="sourceLine" id="cb35-4" data-line-number="4"><span class="kw">summary</span>(M_season)</a>
-<a class="sourceLine" id="cb35-5" data-line-number="5"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb35-6" data-line-number="6"><span class="co">#> Before 161.5108 591.5065 642.3386 677.2794 718.4219 773.4741</span></a>
-<a class="sourceLine" id="cb35-7" data-line-number="7"><span class="co">#> After 264.4892 1011.9878 1084.5125 1130.8355 1180.4037 1258.7803</span></a>
-<a class="sourceLine" id="cb35-8" data-line-number="8"><span class="co">#> attr(,"class")</span></a>
-<a class="sourceLine" id="cb35-9" data-line-number="9"><span class="co">#> [1] "splitSummary"</span></a>
-<a class="sourceLine" id="cb35-10" data-line-number="10"><span class="co">#> attr(,"CL")</span></a>
-<a class="sourceLine" id="cb35-11" data-line-number="11"><span class="co">#> [1] 0.9</span></a>
-<a class="sourceLine" id="cb35-12" data-line-number="12"><span class="co">#> attr(,"vars")</span></a>
-<a class="sourceLine" id="cb35-13" data-line-number="13"><span class="co">#> [1] "Construction"</span></a>
-<a class="sourceLine" id="cb35-14" data-line-number="14"><span class="co">#> attr(,"type")</span></a>
-<a class="sourceLine" id="cb35-15" data-line-number="15"><span class="co">#> [1] "SS"</span></a>
-<a class="sourceLine" id="cb35-16" data-line-number="16"><span class="co">#> attr(,"times")</span></a>
-<a class="sourceLine" id="cb35-17" data-line-number="17"><span class="co">#> [1] 0 127 349</span></a>
-<a class="sourceLine" id="cb35-18" data-line-number="18"><span class="kw">plot</span>(M_season)</a></code></pre></div>
-<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
-<p>Next, a carcass split:</p>
-<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb36-1" data-line-number="1">M_class <-<span class="st"> </span><span class="kw">calcSplits</span>(<span class="dt">M =</span> eM, <span class="dt">split_SS =</span> <span class="ot">NULL</span>,</a>
-<a class="sourceLine" id="cb36-2" data-line-number="2"> <span class="dt">split_CO =</span> <span class="st">"Split"</span>, <span class="dt">data_SS =</span> data_SS, <span class="dt">data_CO =</span> data_CO</a>
-<a class="sourceLine" id="cb36-3" data-line-number="3"> )</a>
-<a class="sourceLine" id="cb36-4" data-line-number="4"><span class="kw">summary</span>(M_class)</a>
-<a class="sourceLine" id="cb36-5" data-line-number="5"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-6" data-line-number="6"><span class="co">#> C1 196 733.2844 787.6625 826.8950 869.513 932.6185</span></a>
-<a class="sourceLine" id="cb36-7" data-line-number="7"><span class="co">#> C2 230 876.6440 936.8937 984.2261 1027.658 1100.5501</span></a>
-<a class="sourceLine" id="cb36-8" data-line-number="8"><span class="co">#> attr(,"class")</span></a>
-<a class="sourceLine" id="cb36-9" data-line-number="9"><span class="co">#> [1] "splitSummary"</span></a>
-<a class="sourceLine" id="cb36-10" data-line-number="10"><span class="co">#> attr(,"CL")</span></a>
-<a class="sourceLine" id="cb36-11" data-line-number="11"><span class="co">#> [1] 0.9</span></a>
-<a class="sourceLine" id="cb36-12" data-line-number="12"><span class="co">#> attr(,"vars")</span></a>
-<a class="sourceLine" id="cb36-13" data-line-number="13"><span class="co">#> [1] "Split"</span></a>
-<a class="sourceLine" id="cb36-14" data-line-number="14"><span class="co">#> attr(,"type")</span></a>
-<a class="sourceLine" id="cb36-15" data-line-number="15"><span class="co">#> [1] "CO"</span></a>
-<a class="sourceLine" id="cb36-16" data-line-number="16"><span class="kw">plot</span>(M_class)</a></code></pre></div>
-<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
-<p>And finally, if two splits are included, the mortality estimation is expanded fully factorially:</p>
-<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb37-1" data-line-number="1">M_SbyC <-<span class="st"> </span><span class="kw">calcSplits</span>(<span class="dt">M =</span> eM, <span class="dt">split_SS =</span> <span class="st">"Construction"</span>,</a>
-<a class="sourceLine" id="cb37-2" data-line-number="2"> <span class="dt">split_CO =</span> <span class="st">"Split"</span>, <span class="dt">data_SS =</span> data_SS, <span class="dt">data_CO =</span> data_CO</a>
-<a class="sourceLine" id="cb37-3" data-line-number="3"> )</a>
-<a class="sourceLine" id="cb37-4" data-line-number="4"><span class="kw">summary</span>(M_SbyC)</a>
-<a class="sourceLine" id="cb37-5" data-line-number="5"><span class="co">#> $C1</span></a>
-<a class="sourceLine" id="cb37-6" data-line-number="6"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb37-7" data-line-number="7"><span class="co">#> Before 80.24521 281.4390 311.0720 332.6060 358.0864 393.4851</span></a>
-<a class="sourceLine" id="cb37-8" data-line-number="8"><span class="co">#> After 115.75479 422.1198 462.7733 492.4431 523.4280 571.1372</span></a>
-<a class="sourceLine" id="cb37-9" data-line-number="9"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb37-10" data-line-number="10"><span class="co">#> $C2</span></a>
-<a class="sourceLine" id="cb37-11" data-line-number="11"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb37-12" data-line-number="12"><span class="co">#> Before 81.26557 288.3014 318.5784 344.4808 369.4505 409.4684</span></a>
-<a class="sourceLine" id="cb37-13" data-line-number="13"><span class="co">#> After 148.73443 558.6102 603.9752 638.6064 671.6607 720.6139</span></a>
-<a class="sourceLine" id="cb37-14" data-line-number="14"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb37-15" data-line-number="15"><span class="co">#> attr(,"class")</span></a>
-<a class="sourceLine" id="cb37-16" data-line-number="16"><span class="co">#> [1] "splitSummary"</span></a>
-<a class="sourceLine" id="cb37-17" data-line-number="17"><span class="co">#> attr(,"CL")</span></a>
-<a class="sourceLine" id="cb37-18" data-line-number="18"><span class="co">#> [1] 0.9</span></a>
-<a class="sourceLine" id="cb37-19" data-line-number="19"><span class="co">#> attr(,"vars")</span></a>
-<a class="sourceLine" id="cb37-20" data-line-number="20"><span class="co">#> [1] "Construction" "Split" </span></a>
-<a class="sourceLine" id="cb37-21" data-line-number="21"><span class="co">#> attr(,"type")</span></a>
-<a class="sourceLine" id="cb37-22" data-line-number="22"><span class="co">#> [1] "SS" "CO"</span></a>
-<a class="sourceLine" id="cb37-23" data-line-number="23"><span class="co">#> attr(,"times")</span></a>
-<a class="sourceLine" id="cb37-24" data-line-number="24"><span class="co">#> [1] 0 127 349</span></a>
-<a class="sourceLine" id="cb37-25" data-line-number="25"><span class="kw">plot</span>(M_SbyC)</a></code></pre></div>
-<p><img 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diff --git a/inst/doc/solar-examples.R b/inst/doc/solar-examples.R
deleted file mode 100644
index c807a077975a8c5a71b0bb2a0fad07d2b9ebb8ad..0000000000000000000000000000000000000000
--- a/inst/doc/solar-examples.R
+++ /dev/null
@@ -1,193 +0,0 @@
-## ----setup, include = FALSE----------------------------------------------
-knitr::opts_chunk$set(
- collapse = TRUE,
- comment = "#>"
-)
-
-rm(list = ls())
-
-library(GenEst)
-vers <- packageVersion("GenEst")
-today <- Sys.Date()
-set.seed(951)
-
-## ------------------------------------------------------------------------
-data(solar_PV)
-names(solar_PV)
-
-## ----pk data-------------------------------------------------------------
-data_SE <- solar_PV$SE
-head(data_SE)
-
-## ----pk one model--------------------------------------------------------
-SE_model <- pkm(p ~ 1, k ~ 1, data = data_SE)
-SE_model
-
-
-## ----pk two models-------------------------------------------------------
-SE_model_set <- pkm(p~Season, k~1, data = data_SE, allCombos = TRUE)
-class(SE_model_set)
-length(SE_model_set)
-names(SE_model_set)
-class(SE_model_set[[1]])
-
-## ----pk set AICc---------------------------------------------------------
-aicc(SE_model_set)
-
-## ----pk size set---------------------------------------------------------
-SE_size_model <- pkm(p ~ Season,
- k ~ 1,
- sizeCol = "Size",
- data = data_SE)
-class(SE_size_model)
-names(SE_size_model) # A list is created with a model set per size class.
-class(SE_size_model$small)
-names(SE_size_model$small) # Each model set contains one model in this case.
-
-
-## ------------------------------------------------------------------------
-SE_size_model_set <- pkm(p ~ Season,
- k ~ 1,
- sizeCol = "Size",
- data = data_SE, allCombos = TRUE)
-aicc(SE_size_model_set)
-SE_models <- list()
-
-## ------------------------------------------------------------------------
-SE_models$small <- SE_size_model_set$small[[2]]
-
-## ----pk size Medium------------------------------------------------------
-SE_models$med <- SE_size_model_set$med[[2]]
-
-## ----pk Size Large-------------------------------------------------------
-SE_models$lrg <- SE_size_model_set$lrg[[1]]
-
-## ----cp data-------------------------------------------------------------
-data_CP <- solar_PV$CP
-head(data_CP)
-
-## ----cp------------------------------------------------------------------
-cpm(l ~ Season, s ~ 1, data = data_CP,
- left = "LastPresent",
- right = "FirstAbsent",
- dist = "weibull")
-
-## ----cp set--------------------------------------------------------------
- CP_weibull_set <- cpm(l ~ Season, s ~ 1, data = data_CP,
- left = "LastPresent",
- right = "FirstAbsent",
- dist = "weibull", allCombos = TRUE)
-class(CP_weibull_set)
-aicc(CP_weibull_set)
-
-## ----cp Size Set---------------------------------------------------------
-CP_size_model_set <- cpm(formula_l = l ~ Season,
- formula_s = s ~ 1,
- left = "LastPresent",
- right = "FirstAbsent",
- dist = c("exponential", "weibull"),
- sizeCol = "Size",
- data = data_CP, allCombos = TRUE)
-class(CP_size_model_set)
-names(CP_size_model_set)
-class(CP_size_model_set$small)
-length(CP_size_model_set$small)
-names(CP_size_model_set$small)
-
-## ------------------------------------------------------------------------
-aicc(CP_size_model_set)
-CP_models <- list()
-
-## ----cp Size Small-------------------------------------------------------
-CP_models$small <- CP_size_model_set$small[[4]]
-
-## ----cp size Medium------------------------------------------------------
-CP_models$med <- CP_size_model_set$med[[4]]
-
-## ----Size Large----------------------------------------------------------
-CP_models$lrg <- CP_size_model_set$lrg[[2]]
-
-## ----Load CO SS and DWP--------------------------------------------------
-data_CO <- solar_PV$CO
-head(data_CO)
-
-## ----SS Data-------------------------------------------------------------
-data_SS <- solar_PV$SS
-data_SS[1:5 , 1:10]
-
-## ----DWP data------------------------------------------------------------
-data_DWP <- solar_PV$DWP
-head(data_DWP)
-
-## ----Arrival Times, options----------------------------------------------
- Mest <- estM(
- nsim = 100, frac = 1,
- data_CO = data_CO, data_SS = data_SS, data_DWP = data_DWP,
- model_SE = SE_models, model_CP = CP_models,
- unitCol = "Unit", sizeCol = "Size",
- COdate = "DateFound", SSdate = "DateSearched"
- )
-
-## ---- fig.show = "hold", fig.height = 4, fig.width = 6, fig.align = 'center'----
-plot(Mest)
-
-## ----Summary - Season----------------------------------------------------
-unique(data_SS[, "Season"])
-
-M_season <- calcSplits(M = Mest,
- split_SS = "Season", data_SS = data_SS,
- split_CO = NULL, data_CO = data_CO
-)
-
-## ----splitFull plot, fig.height = 4, fig.width = 4, fig.align = 'center'----
-plot(M_season)
-
-## ----SplitFull Summary---------------------------------------------------
-summary(M_season, CL = 0.95)
-
-## ----Summary - Weekly----------------------------------------------------
-
-SSdat <- prepSS(data_SS) # Creates an object of type prepSS.
-schedule <- seq(from = 0, to = max(SSdat$days), by = 7)
-tail(schedule)
-
-## ----Summary - Weekly Part 2, fig.height = 4, fig.width = 7, fig.align = 'center'----
-M_week <- calcSplits(M = Mest,
- split_time = schedule,
- data_SS = SSdat,
- data_CO = data_CO
-)
-plot(x = M_week, rate = TRUE)
-
-
-## ----Summary - Unit, fig.height = 4, fig.width = 7, fig.align = 'center'----
-
-M_unit <- calcSplits(M = Mest,
- split_CO = "Unit",
- data_CO = data_CO,
- data_SS = data_SS
-)
-plot(M_unit, rate = FALSE)
-
-## ----individual unit summary---------------------------------------------
-dim(summary(M_unit)) # only 164 arrays had observations.
-
-# A list of the arrays without observed carcasses:
-setdiff(paste0("Unit", 1:300), data_CO$Unit)
-
-# Create summaries for arrays Unit12 and Unit100.
-whichRow <- rownames(summary(M_unit)) %in% c("Unit12", "Unit100")
-summary(M_unit)[whichRow, ]
-
-
-## ----Summary - season and species, fig.height = 5, fig.width = 3, fig.align = 'center'----
-
-M_unit_and_species <- calcSplits(M = Mest,
- split_SS = c("Season"),
- split_CO = c("Size"),
- data_CO = data_CO,
- data_SS = data_SS
-)
-plot(M_unit_and_species, rate = FALSE)
-
-
diff --git a/inst/doc/solar-examples.Rmd b/inst/doc/solar-examples.Rmd
deleted file mode 100644
index e22702fc66cf3747d526b5f7b53578c7574b5171..0000000000000000000000000000000000000000
--- a/inst/doc/solar-examples.Rmd
+++ /dev/null
@@ -1,419 +0,0 @@
----
-title: "GenEst - Tutorial with Solar Example (including Carcass Size)"
-author: "J Mintz, D Dalthorp, J Simonis"
-date: "`r Sys.Date()`"
-output: rmarkdown::html_vignette
-vignette: >
- %\VignetteIndexEntry{GenEst - 2. A Solar Example with Carcass Size}
- %\VignetteEngine{knitr::rmarkdown}
- \usepackage[utf8]{inputenc}
----
-
-```{r setup, include = FALSE}
-knitr::opts_chunk$set(
- collapse = TRUE,
- comment = "#>"
-)
-
-rm(list = ls())
-
-library(GenEst)
-vers <- packageVersion("GenEst")
-today <- Sys.Date()
-set.seed(951)
-```
-
-### Introduction
-
-In this vignette we walk through an example illustrating how **GenEst** command
-line utilities could be used to estimate mortality for different size birds at a
-large field of solar photovoltaic collectors. Our objective is to estimate
-overall mortality, as well as how mortality varies over time, whether it
-constant throughout the facility, and finally how different size classes of
-birds are affected.
-
-The general steps in the analysis are:
-
-1. Construct a model for Searcher Efficiency
-2. Construct a model for Carcass Persistance
-3. Estimate mortality
-4. Specify the type of summary desired (for example, by season and species)
-
-There are five files in total which make up the example dataset, located in `\GenEst\inst\extdata\solar_PV`. For convenience, these files have been made
-available in R as a list,
-
-```{r}
-data(solar_PV)
-names(solar_PV)
-```
-
-### Part 1: Searcher Efficiency Modeling
-
-Searcher efficiency (SE) is modeled as a function of the number of times a
-carcass has been missed in previous searches and any number of covariates.
-The probability of finding a carcass that is present at the time of search is p
-on the first search after carcass arrival and is assumed to decrease by a factor
-of k each time the carcass is missed in searches. (For further background on
-field trials, and information about how to format the results for use with
-GenEst, see the User Guide).
-
-Results of the SE field trials used in this example are stored in the `data_SE`
-data frame:
-
-```{r pk data}
-data_SE <- solar_PV$SE
-head(data_SE)
-```
-
-GenEst provides tools to construct and compare specific individual models, to
-explore which subsets of variables are most useful, and to automatically
-construct entire sets of models. To start we will fit a basic model in which
-the probability of detecting a carcass, `p`, and compounding difficulty to
-detect, `k`, depend only on their respective intercepts (and not other factors
-such as season or size). The function `pkm` is used to create a searcher
-efficiency model, which is returned as a `pkm` object.
-
-```{r pk one model}
-SE_model <- pkm(p ~ 1, k ~ 1, data = data_SE)
-SE_model
-
-```
-To explore whether use of covariate is warranted, `pkm` is used with the
-`allCombos = TRUE`. The specified model will be fit as will models formed
-using all combinations of predictors listed for the `p` and `k` parameters.
-orginal model. For example, `p ~ Season` can be simplified into `p ~ 1`, our
-original model in which `p` is independent of season.
-
-```{r pk two models}
-SE_model_set <- pkm(p~Season, k~1, data = data_SE, allCombos = TRUE)
-class(SE_model_set)
-length(SE_model_set)
-names(SE_model_set)
-class(SE_model_set[[1]])
-```
-The set of models is contained in a `pkmSet` object. We could inspect the two
-models stored in the `pkmSet` individually, or for convenience we can view the
-AICc values simultaneously for all models using the \code{aicc} function.
-Summary plots can be obtained by plotting any of the individual objects or the
-set as well.
-
-```{r pk set AICc}
-aicc(SE_model_set)
-```
-
-Rather than one searcher efficiency model for all birds, it is often preferable
-to fit a seperate model for each size class. The `sizeCol` argument of the
-`pkm` function is the name of the column in `data_SE` that gives the size class
-for each carcass in the SE trials. If a `sizeCol` is provided, `pkm` returns a
-list of separate pk models fit for each size class.
-
-```{r pk size set}
-SE_size_model <- pkm(p ~ Season,
- k ~ 1,
- sizeCol = "Size",
- data = data_SE)
-class(SE_size_model)
-names(SE_size_model) # A list is created with a model set per size class.
-class(SE_size_model$small)
-names(SE_size_model$small) # Each model set contains one model in this case.
-
-```
-To fit all combinations of models for each size class, use `pkm` with a `sizeCol`
-parameter and with `allCombos = T`.
-
-Once we have decided on which models to use for each size class, we store the
-corresponding pkm objects in a list for future use. In this case, we will
-choose the models with the lower AICc.
-```{r}
-SE_size_model_set <- pkm(p ~ Season,
- k ~ 1,
- sizeCol = "Size",
- data = data_SE, allCombos = TRUE)
-aicc(SE_size_model_set)
-SE_models <- list()
-```
-Size small:
-```{r}
-SE_models$small <- SE_size_model_set$small[[2]]
-```
-Size Medium:
-```{r pk size Medium}
-SE_models$med <- SE_size_model_set$med[[2]]
-```
-
-Size Large:
-```{r pk Size Large}
-SE_models$lrg <- SE_size_model_set$lrg[[1]]
-```
-
-
-### Part 2: Carcass Persistence Modeling
-A carcass persistence model estimates the amount of time a carcass would persist
-for, given the conditions under which it arrived. A number of carcasses have
-been placed in the field and periodically checked for scavanging. Results of
-the CP field trials used in this example are stored in the `data_CP` data frame:
-
-```{r cp data}
-data_CP <- solar_PV$CP
-head(data_CP)
-```
-
-\code{LastPresent} and \code{FirstAbsent} represent the left (start) and right
-(end) endpoints of the interval over which a carcass went missing. For further
-information about CP trials and how to format results for use with GenEst, see
-the User Guide (link found on help menu of the GUI, which can be accessed by
-entering `runGenEst()` from the R console).
-
-Four classes of parameteric models may be used for carcass persistance:
-exponential, Weibull, logistic, and lognormal. As with Searcher Efficiency we
-can fit one specific model, test a set of covariates and choose our favorite
-single model, or fit seperate models dependent on size class. First we will
-fit a single Weibull models for all birds. Weibull distributions have two
-parameters, location and scale. We will specify that the location depends on
-season by setting `l ~ season`, but scale only depends on the intercept using
-`s ~ 1`.
-
-```{r cp}
-cpm(l ~ Season, s ~ 1, data = data_CP,
- left = "LastPresent",
- right = "FirstAbsent",
- dist = "weibull")
-```
-
-Next, we try a CP model set considering whether the `season` covariate for
-location is necessary, by comparing the `l ~ season, s ~ 1` to `l ~ 1, s ~ 1`.
-```{r cp set}
- CP_weibull_set <- cpm(l ~ Season, s ~ 1, data = data_CP,
- left = "LastPresent",
- right = "FirstAbsent",
- dist = "weibull", allCombos = TRUE)
-class(CP_weibull_set)
-aicc(CP_weibull_set)
-```
-
-Finally we will construct sets of CP models for each size class, however this
-time we will also consider models based on both exponential and weibull
-distributions. To compare models for multiple distributions, set `dist` to a
-vector of the distribution names to be considered. With a `sizeCol` provided
-and `allCombos = TRUE`, `cpm` returns a list of `cpmSet` objects, one for
-each size class.
-```{r cp Size Set}
-CP_size_model_set <- cpm(formula_l = l ~ Season,
- formula_s = s ~ 1,
- left = "LastPresent",
- right = "FirstAbsent",
- dist = c("exponential", "weibull"),
- sizeCol = "Size",
- data = data_CP, allCombos = TRUE)
-class(CP_size_model_set)
-names(CP_size_model_set)
-class(CP_size_model_set$small)
-length(CP_size_model_set$small)
-names(CP_size_model_set$small)
-```
-
-We now have the flexibility to select models from different families for
-different size classes. We will choose to use the models with lower AICc, which
-requires storing the corresponding `cpm` objects in a list for later use.
-
-
-```{r}
-aicc(CP_size_model_set)
-CP_models <- list()
-```
-Size small:
-```{r cp Size Small}
-CP_models$small <- CP_size_model_set$small[[4]]
-```
-
-Size med:
-```{r cp size Medium}
-CP_models$med <- CP_size_model_set$med[[4]]
-```
-
-Size lrg:
-```{r Size Large}
-CP_models$lrg <- CP_size_model_set$lrg[[2]]
-```
-
-
-### Part 3: Mortality Estimation
-Estimating mortality requires bringing together models, carcass observation
-data (CO), and information on how the data was gathered. In particular the
-search schedule (SS) and proportion of carcasses in searchable areas (the
-density weighted proportion, or DWP), are needed. We will breifly inspect the
-files. Further information on the formatting of the CO, SS, and DWP files can
-be found in the User Guide.
-
-Carcass observations:
-```{r Load CO SS and DWP}
-data_CO <- solar_PV$CO
-head(data_CO)
-```
-Search schedule:
-```{r SS Data}
-data_SS <- solar_PV$SS
-data_SS[1:5 , 1:10]
-```
-(Note that there are 300 arrays columns altogether: Unit1, ..., Unit300)
-
-Density weighted proportion:
-```{r DWP data}
-data_DWP <- solar_PV$DWP
-head(data_DWP)
-```
-
-These elements combine in the function `estM`, producing an object containing
-simulated arrival, detection, and mortality distributions. We also have the
-opportunity to provide the fraction of the facility being surveyed, `frac`, if
-it happens to be less than 100\%. Increasing the number of simulations, `nsim`,
-will improve the accuracy of the estimates but comes at a cost of computer
-runtime.
-
-When estimating mortality, it is not currently possible to mix CP and SE models
-which differ in their dependence on size. Either both models depend on size
-class, or both models must be independent of size class. In this case we will
-choose here to use size dependence.
-
-```{r Arrival Times, options}
- Mest <- estM(
- nsim = 100, frac = 1,
- data_CO = data_CO, data_SS = data_SS, data_DWP = data_DWP,
- model_SE = SE_models, model_CP = CP_models,
- unitCol = "Unit", sizeCol = "Size",
- COdate = "DateFound", SSdate = "DateSearched"
- )
-```
-
-We are now able to get a confidence interval for estimated total mortality by
-taking summary of the estM object. Plotting it shows us us the estimated
-probability density for number of fatalities.
-
-
-```{r, fig.show = "hold", fig.height = 4, fig.width = 6, fig.align = 'center'}
-plot(Mest)
-```
-A point estimate for overall sitewide mortality is listed at the top of the
-plot, satisfying our first objective. The period of inference only covers the
-period over which we have have fatality monitoring data, which in this case is
-from `r min(data_SS$DateSearched)` to `r max(data_SS$DateSearched)`.
-
-
-### Part 4: Summaries
-
-Having calculated the estimated arrival densities for each of the carcases,
-we can now use them to create a variety of summaries. Suppose that we are
-interested in how mortality changes with respect to three kinds of variables:
-
-1. Temporally - by season, or finer resolutions
-2. Spatially - summaries by search unit
-3. Among Size classes, or other groups
-
-To create summaries, we split the data by differnt covariates, using a function
-called `calcSplits`. This requires the simulated mortality `$Mhat` and arrival
-times `$Aj` stored in the `estM` object, plus the search schedule and carcass
-observation data.
-
-Splits to the search schedule (splits in time) are specified by assigning a
-covariate to `split_SS`. These must be variables present in the Search Schedule
-file. To investigate differences in mortality between season, we will set
-`split_SS` to `Season`.
-
-```{r Summary - Season}
-unique(data_SS[, "Season"])
-
-M_season <- calcSplits(M = Mest,
- split_SS = "Season", data_SS = data_SS,
- split_CO = NULL, data_CO = data_CO
-)
-```
-
-Splitting the estM creates a `splitFull` object, a plot of which shows boxplots
-for each season.
-
-```{r splitFull plot, fig.height = 4, fig.width = 4, fig.align = 'center'}
-plot(M_season)
-```
-
-Taking a summary of the `splitFull` object gives us a confidence interval for
-each level of the split covariate. The size of the confidence interval can be
-specified for both plots or summaries using the CL argument.
-
-```{r SplitFull Summary}
-summary(M_season, CL = 0.95)
-```
-
-To get a finer summary of mortality, we need to parse the search schedule, using
-the function `prepSS`. This allows us to specify the exact time intervals over
-which we will split, in this case we will create a weekly summary.
-
-```{r Summary - Weekly}
-
-SSdat <- prepSS(data_SS) # Creates an object of type prepSS.
-schedule <- seq(from = 0, to = max(SSdat$days), by = 7)
-tail(schedule)
-```
-
-When we plot the splitFull object for a split with a custom schedule, we must
-specify that the rate is per split catagory by setting `rate = T`.
-```{r Summary - Weekly Part 2, fig.height = 4, fig.width = 7, fig.align = 'center'}
-M_week <- calcSplits(M = Mest,
- split_time = schedule,
- data_SS = SSdat,
- data_CO = data_CO
-)
-plot(x = M_week, rate = TRUE)
-
-```
-
-Next we will look at at splitting by covariates present in the Carcass
-Observation file. We specify a CO split by assigning split_CO to the name (or
-names) of the variables we wish to split on. Suppose we would like a summary of
-estimated mortality by unit.
-```{r Summary - Unit, fig.height = 4, fig.width = 7, fig.align = 'center'}
-
-M_unit <- calcSplits(M = Mest,
- split_CO = "Unit",
- data_CO = data_CO,
- data_SS = data_SS
-)
-plot(M_unit, rate = FALSE)
-```
-There are 300 units in this example, each one gets a boxplot when we plot the
-splitFull. For those arrays which have at least one observation, we can create
-a summary. In this case we will only create a summary for arrays 8 and 100.
-
-```{r individual unit summary}
-dim(summary(M_unit)) # only 164 arrays had observations.
-
-# A list of the arrays without observed carcasses:
-setdiff(paste0("Unit", 1:300), data_CO$Unit)
-
-# Create summaries for arrays Unit12 and Unit100.
-whichRow <- rownames(summary(M_unit)) %in% c("Unit12", "Unit100")
-summary(M_unit)[whichRow, ]
-
-```
-
-It is possible to create summaries that split on both Carcass Observation
-variables and Search Schedule variables. To do so, include both a `split_SS`
-and a `split_CO` argument.
-
-```{r Summary - season and species, fig.height = 5, fig.width = 3, fig.align = 'center'}
-
-M_unit_and_species <- calcSplits(M = Mest,
- split_SS = c("Season"),
- split_CO = c("Size"),
- data_CO = data_CO,
- data_SS = data_SS
-)
-plot(M_unit_and_species, rate = FALSE)
-
-```
-
-Two CO variables can be compared simultaneously by specifying an ordered pair of
-covariates for `split_CO`, however currently there are a limited total number
-(two) of splits which can be allocated among temporal or carcass covariates.
-
-
-
diff --git a/inst/doc/solar-examples.html b/inst/doc/solar-examples.html
deleted file mode 100644
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-<meta name="generator" content="pandoc" />
-
-<meta name="viewport" content="width=device-width, initial-scale=1">
-
-<meta name="author" content="J Mintz, D Dalthorp, J Simonis" />
-
-<meta name="date" content="2018-12-05" />
-
-<title>GenEst - Tutorial with Solar Example (including Carcass Size)</title>
-
-
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-<h1 class="title toc-ignore">GenEst - Tutorial with Solar Example (including Carcass Size)</h1>
-<h4 class="author"><em>J Mintz, D Dalthorp, J Simonis</em></h4>
-<h4 class="date"><em>2018-12-05</em></h4>
-
-
-
-<div id="introduction" class="section level3">
-<h3>Introduction</h3>
-<p>In this vignette we walk through an example illustrating how <strong>GenEst</strong> command line utilities could be used to estimate mortality for different size birds at a large field of solar photovoltaic collectors. Our objective is to estimate overall mortality, as well as how mortality varies over time, whether it constant throughout the facility, and finally how different size classes of birds are affected.</p>
-<p>The general steps in the analysis are:</p>
-<ol style="list-style-type: decimal">
-<li>Construct a model for Searcher Efficiency</li>
-<li>Construct a model for Carcass Persistance</li>
-<li>Estimate mortality</li>
-<li>Specify the type of summary desired (for example, by season and species)</li>
-</ol>
-<p>There are five files in total which make up the example dataset, located in <code>\GenEst\inst\extdata\solar_PV</code>. For convenience, these files have been made available in R as a list,</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw">data</span>(solar_PV)</a>
-<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw">names</span>(solar_PV)</a>
-<a class="sourceLine" id="cb1-3" data-line-number="3"><span class="co">#> [1] "SE" "CP" "SS" "DWP" "CO"</span></a></code></pre></div>
-</div>
-<div id="part-1-searcher-efficiency-modeling" class="section level3">
-<h3>Part 1: Searcher Efficiency Modeling</h3>
-<p>Searcher efficiency (SE) is modeled as a function of the number of times a carcass has been missed in previous searches and any number of covariates. The probability of finding a carcass that is present at the time of search is p on the first search after carcass arrival and is assumed to decrease by a factor of k each time the carcass is missed in searches. (For further background on field trials, and information about how to format the results for use with GenEst, see the User Guide).</p>
-<p>Results of the SE field trials used in this example are stored in the <code>data_SE</code> data frame:</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1">data_SE <-<span class="st"> </span>solar_PV<span class="op">$</span>SE</a>
-<a class="sourceLine" id="cb2-2" data-line-number="2"><span class="kw">head</span>(data_SE)</a>
-<a class="sourceLine" id="cb2-3" data-line-number="3"><span class="co">#> seID Season Size Search1 Search2 Search3 Search4 Distance</span></a>
-<a class="sourceLine" id="cb2-4" data-line-number="4"><span class="co">#> 1 se1 winter small 1 NA NA NA 17</span></a>
-<a class="sourceLine" id="cb2-5" data-line-number="5"><span class="co">#> 2 se2 winter small 1 NA NA NA 26</span></a>
-<a class="sourceLine" id="cb2-6" data-line-number="6"><span class="co">#> 3 se3 winter small 0 1 NA NA 54</span></a>
-<a class="sourceLine" id="cb2-7" data-line-number="7"><span class="co">#> 4 se4 winter small 0 1 NA NA 28</span></a>
-<a class="sourceLine" id="cb2-8" data-line-number="8"><span class="co">#> 5 se5 winter small 1 NA NA NA 46</span></a>
-<a class="sourceLine" id="cb2-9" data-line-number="9"><span class="co">#> 6 se6 winter small 1 NA NA NA 40</span></a></code></pre></div>
-<p>GenEst provides tools to construct and compare specific individual models, to explore which subsets of variables are most useful, and to automatically construct entire sets of models. To start we will fit a basic model in which the probability of detecting a carcass, <code>p</code>, and compounding difficulty to detect, <code>k</code>, depend only on their respective intercepts (and not other factors such as season or size). The function <code>pkm</code> is used to create a searcher efficiency model, which is returned as a <code>pkm</code> object.</p>
-<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1">SE_model <-<span class="st"> </span><span class="kw">pkm</span>(p <span class="op">~</span><span class="st"> </span><span class="dv">1</span>, k <span class="op">~</span><span class="st"> </span><span class="dv">1</span>, <span class="dt">data =</span> data_SE)</a>
-<a class="sourceLine" id="cb3-2" data-line-number="2">SE_model</a>
-<a class="sourceLine" id="cb3-3" data-line-number="3"><span class="co">#> $call</span></a>
-<a class="sourceLine" id="cb3-4" data-line-number="4"><span class="co">#> pkm0(formula_p = formula_p, formula_k = formula_k, data = data, </span></a>
-<a class="sourceLine" id="cb3-5" data-line-number="5"><span class="co">#> obsCol = obsCol, kFixed = kFixed, kInit = kInit, CL = CL, </span></a>
-<a class="sourceLine" id="cb3-6" data-line-number="6"><span class="co">#> quiet = quiet)</span></a>
-<a class="sourceLine" id="cb3-7" data-line-number="7"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="co">#> $formula_p</span></a>
-<a class="sourceLine" id="cb3-9" data-line-number="9"><span class="co">#> p ~ 1</span></a>
-<a class="sourceLine" id="cb3-10" data-line-number="10"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb3-11" data-line-number="11"><span class="co">#> $formula_k</span></a>
-<a class="sourceLine" id="cb3-12" data-line-number="12"><span class="co">#> k ~ 1</span></a>
-<a class="sourceLine" id="cb3-13" data-line-number="13"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb3-14" data-line-number="14"><span class="co">#> $predictors</span></a>
-<a class="sourceLine" id="cb3-15" data-line-number="15"><span class="co">#> character(0)</span></a>
-<a class="sourceLine" id="cb3-16" data-line-number="16"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb3-17" data-line-number="17"><span class="co">#> $AICc</span></a>
-<a class="sourceLine" id="cb3-18" data-line-number="18"><span class="co">#> [1] 489</span></a>
-<a class="sourceLine" id="cb3-19" data-line-number="19"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb3-20" data-line-number="20"><span class="co">#> $convergence</span></a>
-<a class="sourceLine" id="cb3-21" data-line-number="21"><span class="co">#> [1] 0</span></a>
-<a class="sourceLine" id="cb3-22" data-line-number="22"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb3-23" data-line-number="23"><span class="co">#> $cell_pk</span></a>
-<a class="sourceLine" id="cb3-24" data-line-number="24"><span class="co">#> cell n p_median p_lower p_upper k_median k_lower k_upper</span></a>
-<a class="sourceLine" id="cb3-25" data-line-number="25"><span class="co">#> 1 all 240 0.665 0.615 0.711 0.846 0.742 0.913</span></a>
-<a class="sourceLine" id="cb3-26" data-line-number="26"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb3-27" data-line-number="27"><span class="co">#> $CL</span></a>
-<a class="sourceLine" id="cb3-28" data-line-number="28"><span class="co">#> [1] 0.9</span></a></code></pre></div>
-<p>To explore whether use of covariate is warranted, <code>pkm</code> is used with the <code>allCombos = TRUE</code>. The specified model will be fit as will models formed using all combinations of predictors listed for the <code>p</code> and <code>k</code> parameters. orginal model. For example, <code>p ~ Season</code> can be simplified into <code>p ~ 1</code>, our original model in which <code>p</code> is independent of season.</p>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1">SE_model_set <-<span class="st"> </span><span class="kw">pkm</span>(p<span class="op">~</span>Season, k<span class="op">~</span><span class="dv">1</span>, <span class="dt">data =</span> data_SE, <span class="dt">allCombos =</span> <span class="ot">TRUE</span>)</a>
-<a class="sourceLine" id="cb4-2" data-line-number="2"><span class="kw">class</span>(SE_model_set)</a>
-<a class="sourceLine" id="cb4-3" data-line-number="3"><span class="co">#> [1] "pkmSet" "list"</span></a>
-<a class="sourceLine" id="cb4-4" data-line-number="4"><span class="kw">length</span>(SE_model_set)</a>
-<a class="sourceLine" id="cb4-5" data-line-number="5"><span class="co">#> [1] 2</span></a>
-<a class="sourceLine" id="cb4-6" data-line-number="6"><span class="kw">names</span>(SE_model_set)</a>
-<a class="sourceLine" id="cb4-7" data-line-number="7"><span class="co">#> [1] "p ~ Season; k ~ 1" "p ~ 1; k ~ 1"</span></a>
-<a class="sourceLine" id="cb4-8" data-line-number="8"><span class="kw">class</span>(SE_model_set[[<span class="dv">1</span>]])</a>
-<a class="sourceLine" id="cb4-9" data-line-number="9"><span class="co">#> [1] "pkm" "list"</span></a></code></pre></div>
-<p>The set of models is contained in a <code>pkmSet</code> object. We could inspect the two models stored in the <code>pkmSet</code> individually, or for convenience we can view the AICc values simultaneously for all models using the function. Summary plots can be obtained by plotting any of the individual objects or the set as well.</p>
-<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="kw">aicc</span>(SE_model_set)</a>
-<a class="sourceLine" id="cb5-2" data-line-number="2"><span class="co">#> p Formula k Formula AICc <U+0394>AICc</span></a>
-<a class="sourceLine" id="cb5-3" data-line-number="3"><span class="co">#> 2 p ~ 1 k ~ 1 489.00 0.00</span></a>
-<a class="sourceLine" id="cb5-4" data-line-number="4"><span class="co">#> 1 p ~ Season k ~ 1 492.11 3.11</span></a></code></pre></div>
-<p>Rather than one searcher efficiency model for all birds, it is often preferable to fit a seperate model for each size class. The <code>sizeCol</code> argument of the <code>pkm</code> function is the name of the column in <code>data_SE</code> that gives the size class for each carcass in the SE trials. If a <code>sizeCol</code> is provided, <code>pkm</code> returns a list of separate pk models fit for each size class.</p>
-<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1">SE_size_model <-<span class="st"> </span><span class="kw">pkm</span>(p <span class="op">~</span><span class="st"> </span>Season,</a>
-<a class="sourceLine" id="cb6-2" data-line-number="2"> k <span class="op">~</span><span class="st"> </span><span class="dv">1</span>,</a>
-<a class="sourceLine" id="cb6-3" data-line-number="3"> <span class="dt">sizeCol =</span> <span class="st">"Size"</span>,</a>
-<a class="sourceLine" id="cb6-4" data-line-number="4"> <span class="dt">data =</span> data_SE)</a>
-<a class="sourceLine" id="cb6-5" data-line-number="5"><span class="kw">class</span>(SE_size_model)</a>
-<a class="sourceLine" id="cb6-6" data-line-number="6"><span class="co">#> [1] "pkmSize" "list"</span></a>
-<a class="sourceLine" id="cb6-7" data-line-number="7"><span class="kw">names</span>(SE_size_model) <span class="co"># A list is created with a model set per size class.</span></a>
-<a class="sourceLine" id="cb6-8" data-line-number="8"><span class="co">#> [1] "lrg" "med" "small"</span></a>
-<a class="sourceLine" id="cb6-9" data-line-number="9"><span class="kw">class</span>(SE_size_model<span class="op">$</span>small)</a>
-<a class="sourceLine" id="cb6-10" data-line-number="10"><span class="co">#> [1] "pkm" "list"</span></a>
-<a class="sourceLine" id="cb6-11" data-line-number="11"><span class="kw">names</span>(SE_size_model<span class="op">$</span>small) <span class="co"># Each model set contains one model in this case.</span></a>
-<a class="sourceLine" id="cb6-12" data-line-number="12"><span class="co">#> [1] "call" "data" "data0" "formula_p" </span></a>
-<a class="sourceLine" id="cb6-13" data-line-number="13"><span class="co">#> [5] "formula_k" "predictors" "predictors_p" "predictors_k"</span></a>
-<a class="sourceLine" id="cb6-14" data-line-number="14"><span class="co">#> [9] "AIC" "AICc" "convergence" "varbeta" </span></a>
-<a class="sourceLine" id="cb6-15" data-line-number="15"><span class="co">#> [13] "cellMM_p" "cellMM_k" "nbeta_p" "nbeta_k" </span></a>
-<a class="sourceLine" id="cb6-16" data-line-number="16"><span class="co">#> [17] "betahat_p" "betahat_k" "levels_p" "cells" </span></a>
-<a class="sourceLine" id="cb6-17" data-line-number="17"><span class="co">#> [21] "ncell" "cell_pk" "CL" "observations"</span></a>
-<a class="sourceLine" id="cb6-18" data-line-number="18"><span class="co">#> [25] "carcCells" "loglik" "pOnly"</span></a></code></pre></div>
-<p>To fit all combinations of models for each size class, use <code>pkm</code> with a <code>sizeCol</code> parameter and with <code>allCombos = T</code>.</p>
-<p>Once we have decided on which models to use for each size class, we store the corresponding pkm objects in a list for future use. In this case, we will choose the models with the lower AICc.</p>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1">SE_size_model_set <-<span class="st"> </span><span class="kw">pkm</span>(p <span class="op">~</span><span class="st"> </span>Season,</a>
-<a class="sourceLine" id="cb7-2" data-line-number="2"> k <span class="op">~</span><span class="st"> </span><span class="dv">1</span>,</a>
-<a class="sourceLine" id="cb7-3" data-line-number="3"> <span class="dt">sizeCol =</span> <span class="st">"Size"</span>,</a>
-<a class="sourceLine" id="cb7-4" data-line-number="4"> <span class="dt">data =</span> data_SE, <span class="dt">allCombos =</span> <span class="ot">TRUE</span>)</a>
-<a class="sourceLine" id="cb7-5" data-line-number="5"><span class="kw">aicc</span>(SE_size_model_set)</a>
-<a class="sourceLine" id="cb7-6" data-line-number="6"><span class="co">#> $lrg</span></a>
-<a class="sourceLine" id="cb7-7" data-line-number="7"><span class="co">#> p Formula k Formula AICc <U+0394>AICc</span></a>
-<a class="sourceLine" id="cb7-8" data-line-number="8"><span class="co">#> 1 p ~ Season k ~ 1 95.65 0.00</span></a>
-<a class="sourceLine" id="cb7-9" data-line-number="9"><span class="co">#> 2 p ~ 1 k ~ 1 96.47 0.82</span></a>
-<a class="sourceLine" id="cb7-10" data-line-number="10"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb7-11" data-line-number="11"><span class="co">#> $med</span></a>
-<a class="sourceLine" id="cb7-12" data-line-number="12"><span class="co">#> p Formula k Formula AICc <U+0394>AICc</span></a>
-<a class="sourceLine" id="cb7-13" data-line-number="13"><span class="co">#> 2 p ~ 1 k ~ 1 178.62 0.00</span></a>
-<a class="sourceLine" id="cb7-14" data-line-number="14"><span class="co">#> 1 p ~ Season k ~ 1 182.40 3.78</span></a>
-<a class="sourceLine" id="cb7-15" data-line-number="15"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb7-16" data-line-number="16"><span class="co">#> $small</span></a>
-<a class="sourceLine" id="cb7-17" data-line-number="17"><span class="co">#> p Formula k Formula AICc <U+0394>AICc</span></a>
-<a class="sourceLine" id="cb7-18" data-line-number="18"><span class="co">#> 2 p ~ 1 k ~ 1 202.17 0.00</span></a>
-<a class="sourceLine" id="cb7-19" data-line-number="19"><span class="co">#> 1 p ~ Season k ~ 1 206.48 4.31</span></a>
-<a class="sourceLine" id="cb7-20" data-line-number="20">SE_models <-<span class="st"> </span><span class="kw">list</span>()</a></code></pre></div>
-<p>Size small:</p>
-<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" data-line-number="1">SE_models<span class="op">$</span>small <-<span class="st"> </span>SE_size_model_set<span class="op">$</span>small[[<span class="dv">2</span>]]</a></code></pre></div>
-<p>Size Medium:</p>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" data-line-number="1">SE_models<span class="op">$</span>med <-<span class="st"> </span>SE_size_model_set<span class="op">$</span>med[[<span class="dv">2</span>]]</a></code></pre></div>
-<p>Size Large:</p>
-<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" data-line-number="1">SE_models<span class="op">$</span>lrg <-<span class="st"> </span>SE_size_model_set<span class="op">$</span>lrg[[<span class="dv">1</span>]]</a></code></pre></div>
-</div>
-<div id="part-2-carcass-persistence-modeling" class="section level3">
-<h3>Part 2: Carcass Persistence Modeling</h3>
-<p>A carcass persistence model estimates the amount of time a carcass would persist for, given the conditions under which it arrived. A number of carcasses have been placed in the field and periodically checked for scavanging. Results of the CP field trials used in this example are stored in the <code>data_CP</code> data frame:</p>
-<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" data-line-number="1">data_CP <-<span class="st"> </span>solar_PV<span class="op">$</span>CP</a>
-<a class="sourceLine" id="cb11-2" data-line-number="2"><span class="kw">head</span>(data_CP)</a>
-<a class="sourceLine" id="cb11-3" data-line-number="3"><span class="co">#> cpID Season Size LastPresent FirstAbsent</span></a>
-<a class="sourceLine" id="cb11-4" data-line-number="4"><span class="co">#> 1 cp1 winter small 0.00 1.05</span></a>
-<a class="sourceLine" id="cb11-5" data-line-number="5"><span class="co">#> 2 cp2 winter small 21.02 27.97</span></a>
-<a class="sourceLine" id="cb11-6" data-line-number="6"><span class="co">#> 3 cp3 winter small 4.03 7.01</span></a>
-<a class="sourceLine" id="cb11-7" data-line-number="7"><span class="co">#> 4 cp4 winter small 0.00 0.97</span></a>
-<a class="sourceLine" id="cb11-8" data-line-number="8"><span class="co">#> 5 cp5 winter small 27.99 NA</span></a>
-<a class="sourceLine" id="cb11-9" data-line-number="9"><span class="co">#> 6 cp6 winter small 10.02 13.99</span></a></code></pre></div>
-<p> and represent the left (start) and right (end) endpoints of the interval over which a carcass went missing. For further information about CP trials and how to format results for use with GenEst, see the User Guide (link found on help menu of the GUI, which can be accessed by entering <code>runGenEst()</code> from the R console).</p>
-<p>Four classes of parameteric models may be used for carcass persistance: exponential, Weibull, logistic, and lognormal. As with Searcher Efficiency we can fit one specific model, test a set of covariates and choose our favorite single model, or fit seperate models dependent on size class. First we will fit a single Weibull models for all birds. Weibull distributions have two parameters, location and scale. We will specify that the location depends on season by setting <code>l ~ season</code>, but scale only depends on the intercept using <code>s ~ 1</code>.</p>
-<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" data-line-number="1"><span class="kw">cpm</span>(l <span class="op">~</span><span class="st"> </span>Season, s <span class="op">~</span><span class="st"> </span><span class="dv">1</span>, <span class="dt">data =</span> data_CP,</a>
-<a class="sourceLine" id="cb12-2" data-line-number="2"> <span class="dt">left =</span> <span class="st">"LastPresent"</span>,</a>
-<a class="sourceLine" id="cb12-3" data-line-number="3"> <span class="dt">right =</span> <span class="st">"FirstAbsent"</span>,</a>
-<a class="sourceLine" id="cb12-4" data-line-number="4"> <span class="dt">dist =</span> <span class="st">"weibull"</span>)</a>
-<a class="sourceLine" id="cb12-5" data-line-number="5"><span class="co">#> $call</span></a>
-<a class="sourceLine" id="cb12-6" data-line-number="6"><span class="co">#> cpm0(formula_l = formula_l, formula_s = formula_s, data = data, </span></a>
-<a class="sourceLine" id="cb12-7" data-line-number="7"><span class="co">#> left = left, right = right, dist = dist, CL = CL, quiet = quiet)</span></a>
-<a class="sourceLine" id="cb12-8" data-line-number="8"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb12-9" data-line-number="9"><span class="co">#> $formula_l</span></a>
-<a class="sourceLine" id="cb12-10" data-line-number="10"><span class="co">#> l ~ Season</span></a>
-<a class="sourceLine" id="cb12-11" data-line-number="11"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb12-12" data-line-number="12"><span class="co">#> $formula_s</span></a>
-<a class="sourceLine" id="cb12-13" data-line-number="13"><span class="co">#> s ~ 1</span></a>
-<a class="sourceLine" id="cb12-14" data-line-number="14"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb12-15" data-line-number="15"><span class="co">#> $distribution</span></a>
-<a class="sourceLine" id="cb12-16" data-line-number="16"><span class="co">#> [1] "weibull"</span></a>
-<a class="sourceLine" id="cb12-17" data-line-number="17"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb12-18" data-line-number="18"><span class="co">#> $predictors</span></a>
-<a class="sourceLine" id="cb12-19" data-line-number="19"><span class="co">#> [1] "Season"</span></a>
-<a class="sourceLine" id="cb12-20" data-line-number="20"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb12-21" data-line-number="21"><span class="co">#> $AICc</span></a>
-<a class="sourceLine" id="cb12-22" data-line-number="22"><span class="co">#> [1] 1051.61</span></a>
-<a class="sourceLine" id="cb12-23" data-line-number="23"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb12-24" data-line-number="24"><span class="co">#> $convergence</span></a>
-<a class="sourceLine" id="cb12-25" data-line-number="25"><span class="co">#> [1] 0</span></a>
-<a class="sourceLine" id="cb12-26" data-line-number="26"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb12-27" data-line-number="27"><span class="co">#> $cell_ls</span></a>
-<a class="sourceLine" id="cb12-28" data-line-number="28"><span class="co">#> cell n l_median l_lower l_upper s_median s_lower s_upper</span></a>
-<a class="sourceLine" id="cb12-29" data-line-number="29"><span class="co">#> 1 fall 60 2.473 2.087 2.859 1.637 1.457 1.838</span></a>
-<a class="sourceLine" id="cb12-30" data-line-number="30"><span class="co">#> 2 spring 60 2.801 2.395 3.208 1.637 1.457 1.838</span></a>
-<a class="sourceLine" id="cb12-31" data-line-number="31"><span class="co">#> 3 summer 60 2.689 2.296 3.083 1.637 1.457 1.838</span></a>
-<a class="sourceLine" id="cb12-32" data-line-number="32"><span class="co">#> 4 winter 60 2.509 2.127 2.891 1.637 1.457 1.838</span></a>
-<a class="sourceLine" id="cb12-33" data-line-number="33"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb12-34" data-line-number="34"><span class="co">#> $cell_ab</span></a>
-<a class="sourceLine" id="cb12-35" data-line-number="35"><span class="co">#> cell n pda_median pda_lower pda_upper pdb_median pdb_lower pdb_upper</span></a>
-<a class="sourceLine" id="cb12-36" data-line-number="36"><span class="co">#> 1 fall 60 0.611 0.686 0.544 11.858 8.061 17.444</span></a>
-<a class="sourceLine" id="cb12-37" data-line-number="37"><span class="co">#> 2 spring 60 0.611 0.686 0.544 16.461 10.968 24.730</span></a>
-<a class="sourceLine" id="cb12-38" data-line-number="38"><span class="co">#> 3 summer 60 0.611 0.686 0.544 14.717 9.934 21.824</span></a>
-<a class="sourceLine" id="cb12-39" data-line-number="39"><span class="co">#> 4 winter 60 0.611 0.686 0.544 12.293 8.390 18.011</span></a>
-<a class="sourceLine" id="cb12-40" data-line-number="40"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb12-41" data-line-number="41"><span class="co">#> $CL</span></a>
-<a class="sourceLine" id="cb12-42" data-line-number="42"><span class="co">#> [1] 0.9</span></a>
-<a class="sourceLine" id="cb12-43" data-line-number="43"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb12-44" data-line-number="44"><span class="co">#> $cell_desc</span></a>
-<a class="sourceLine" id="cb12-45" data-line-number="45"><span class="co">#> cell medianCP r1 r3 r7 r14 r28</span></a>
-<a class="sourceLine" id="cb12-46" data-line-number="46"><span class="co">#> 1 fall 6.508736 0.8733664 0.7695707 0.6489244 0.5240883 0.3856577</span></a>
-<a class="sourceLine" id="cb12-47" data-line-number="47"><span class="co">#> 2 spring 9.035276 0.8948948 0.8063180 0.7000989 0.5856841 0.4518313</span></a>
-<a class="sourceLine" id="cb12-48" data-line-number="48"><span class="co">#> 3 summer 8.078012 0.8879564 0.7943671 0.6832539 0.5651042 0.4292548</span></a>
-<a class="sourceLine" id="cb12-49" data-line-number="49"><span class="co">#> 4 winter 6.747503 0.8759150 0.7738696 0.6548159 0.5310385 0.3929133</span></a></code></pre></div>
-<p>Next, we try a CP model set considering whether the <code>season</code> covariate for location is necessary, by comparing the <code>l ~ season, s ~ 1</code> to <code>l ~ 1, s ~ 1</code>.</p>
-<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" data-line-number="1"> CP_weibull_set <-<span class="st"> </span><span class="kw">cpm</span>(l <span class="op">~</span><span class="st"> </span>Season, s <span class="op">~</span><span class="st"> </span><span class="dv">1</span>, <span class="dt">data =</span> data_CP,</a>
-<a class="sourceLine" id="cb13-2" data-line-number="2"> <span class="dt">left =</span> <span class="st">"LastPresent"</span>,</a>
-<a class="sourceLine" id="cb13-3" data-line-number="3"> <span class="dt">right =</span> <span class="st">"FirstAbsent"</span>,</a>
-<a class="sourceLine" id="cb13-4" data-line-number="4"> <span class="dt">dist =</span> <span class="st">"weibull"</span>, <span class="dt">allCombos =</span> <span class="ot">TRUE</span>)</a>
-<a class="sourceLine" id="cb13-5" data-line-number="5"><span class="kw">class</span>(CP_weibull_set)</a>
-<a class="sourceLine" id="cb13-6" data-line-number="6"><span class="co">#> [1] "cpmSet" "list"</span></a>
-<a class="sourceLine" id="cb13-7" data-line-number="7"><span class="kw">aicc</span>(CP_weibull_set)</a>
-<a class="sourceLine" id="cb13-8" data-line-number="8"><span class="co">#> Distribution Location Formula Scale Formula AICc <U+0394>AICc</span></a>
-<a class="sourceLine" id="cb13-9" data-line-number="9"><span class="co">#> 2 weibull l ~ 1 s ~ 1 1046.65 0.00</span></a>
-<a class="sourceLine" id="cb13-10" data-line-number="10"><span class="co">#> 1 weibull l ~ Season s ~ 1 1051.61 4.96</span></a></code></pre></div>
-<p>Finally we will construct sets of CP models for each size class, however this time we will also consider models based on both exponential and weibull distributions. To compare models for multiple distributions, set <code>dist</code> to a vector of the distribution names to be considered. With a <code>sizeCol</code> provided and <code>allCombos = TRUE</code>, <code>cpm</code> returns a list of <code>cpmSet</code> objects, one for each size class.</p>
-<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb14-1" data-line-number="1">CP_size_model_set <-<span class="st"> </span><span class="kw">cpm</span>(<span class="dt">formula_l =</span> l <span class="op">~</span><span class="st"> </span>Season,</a>
-<a class="sourceLine" id="cb14-2" data-line-number="2"> <span class="dt">formula_s =</span> s <span class="op">~</span><span class="st"> </span><span class="dv">1</span>, </a>
-<a class="sourceLine" id="cb14-3" data-line-number="3"> <span class="dt">left =</span> <span class="st">"LastPresent"</span>,</a>
-<a class="sourceLine" id="cb14-4" data-line-number="4"> <span class="dt">right =</span> <span class="st">"FirstAbsent"</span>,</a>
-<a class="sourceLine" id="cb14-5" data-line-number="5"> <span class="dt">dist =</span> <span class="kw">c</span>(<span class="st">"exponential"</span>, <span class="st">"weibull"</span>),</a>
-<a class="sourceLine" id="cb14-6" data-line-number="6"> <span class="dt">sizeCol =</span> <span class="st">"Size"</span>,</a>
-<a class="sourceLine" id="cb14-7" data-line-number="7"> <span class="dt">data =</span> data_CP, <span class="dt">allCombos =</span> <span class="ot">TRUE</span>)</a>
-<a class="sourceLine" id="cb14-8" data-line-number="8"><span class="kw">class</span>(CP_size_model_set)</a>
-<a class="sourceLine" id="cb14-9" data-line-number="9"><span class="co">#> [1] "cpmSetSize" "list"</span></a>
-<a class="sourceLine" id="cb14-10" data-line-number="10"><span class="kw">names</span>(CP_size_model_set)</a>
-<a class="sourceLine" id="cb14-11" data-line-number="11"><span class="co">#> [1] "lrg" "med" "small"</span></a>
-<a class="sourceLine" id="cb14-12" data-line-number="12"><span class="kw">class</span>(CP_size_model_set<span class="op">$</span>small)</a>
-<a class="sourceLine" id="cb14-13" data-line-number="13"><span class="co">#> [1] "cpmSet" "list"</span></a>
-<a class="sourceLine" id="cb14-14" data-line-number="14"><span class="kw">length</span>(CP_size_model_set<span class="op">$</span>small)</a>
-<a class="sourceLine" id="cb14-15" data-line-number="15"><span class="co">#> [1] 4</span></a>
-<a class="sourceLine" id="cb14-16" data-line-number="16"><span class="kw">names</span>(CP_size_model_set<span class="op">$</span>small)</a>
-<a class="sourceLine" id="cb14-17" data-line-number="17"><span class="co">#> [1] "dist: exponential; l ~ Season; NULL"</span></a>
-<a class="sourceLine" id="cb14-18" data-line-number="18"><span class="co">#> [2] "dist: exponential; l ~ 1; NULL" </span></a>
-<a class="sourceLine" id="cb14-19" data-line-number="19"><span class="co">#> [3] "dist: weibull; l ~ Season; s ~ 1" </span></a>
-<a class="sourceLine" id="cb14-20" data-line-number="20"><span class="co">#> [4] "dist: weibull; l ~ 1; s ~ 1"</span></a></code></pre></div>
-<p>We now have the flexibility to select models from different families for different size classes. We will choose to use the models with lower AICc, which requires storing the corresponding <code>cpm</code> objects in a list for later use.</p>
-<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb15-1" data-line-number="1"><span class="kw">aicc</span>(CP_size_model_set)</a>
-<a class="sourceLine" id="cb15-2" data-line-number="2"><span class="co">#> $lrg</span></a>
-<a class="sourceLine" id="cb15-3" data-line-number="3"><span class="co">#> Distribution Location Formula Scale Formula AICc <U+0394>AICc</span></a>
-<a class="sourceLine" id="cb15-4" data-line-number="4"><span class="co">#> 2 exponential l ~ 1 NULL 294.59 0.00</span></a>
-<a class="sourceLine" id="cb15-5" data-line-number="5"><span class="co">#> 4 weibull l ~ 1 s ~ 1 295.60 1.01</span></a>
-<a class="sourceLine" id="cb15-6" data-line-number="6"><span class="co">#> 1 exponential l ~ Season NULL 299.61 5.02</span></a>
-<a class="sourceLine" id="cb15-7" data-line-number="7"><span class="co">#> 3 weibull l ~ Season s ~ 1 300.86 6.27</span></a>
-<a class="sourceLine" id="cb15-8" data-line-number="8"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb15-9" data-line-number="9"><span class="co">#> $med</span></a>
-<a class="sourceLine" id="cb15-10" data-line-number="10"><span class="co">#> Distribution Location Formula Scale Formula AICc <U+0394>AICc</span></a>
-<a class="sourceLine" id="cb15-11" data-line-number="11"><span class="co">#> 4 weibull l ~ 1 s ~ 1 352.44 0.00</span></a>
-<a class="sourceLine" id="cb15-12" data-line-number="12"><span class="co">#> 3 weibull l ~ Season s ~ 1 356.47 4.03</span></a>
-<a class="sourceLine" id="cb15-13" data-line-number="13"><span class="co">#> 2 exponential l ~ 1 NULL 358.25 5.81</span></a>
-<a class="sourceLine" id="cb15-14" data-line-number="14"><span class="co">#> 1 exponential l ~ Season NULL 361.45 9.01</span></a>
-<a class="sourceLine" id="cb15-15" data-line-number="15"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb15-16" data-line-number="16"><span class="co">#> $small</span></a>
-<a class="sourceLine" id="cb15-17" data-line-number="17"><span class="co">#> Distribution Location Formula Scale Formula AICc <U+0394>AICc</span></a>
-<a class="sourceLine" id="cb15-18" data-line-number="18"><span class="co">#> 4 weibull l ~ 1 s ~ 1 346.02 0.00</span></a>
-<a class="sourceLine" id="cb15-19" data-line-number="19"><span class="co">#> 3 weibull l ~ Season s ~ 1 348.88 2.86</span></a>
-<a class="sourceLine" id="cb15-20" data-line-number="20"><span class="co">#> 1 exponential l ~ Season NULL 380.56 34.54</span></a>
-<a class="sourceLine" id="cb15-21" data-line-number="21"><span class="co">#> 2 exponential l ~ 1 NULL 382.04 36.02</span></a>
-<a class="sourceLine" id="cb15-22" data-line-number="22">CP_models <-<span class="st"> </span><span class="kw">list</span>()</a></code></pre></div>
-<p>Size small:</p>
-<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb16-1" data-line-number="1">CP_models<span class="op">$</span>small <-<span class="st"> </span>CP_size_model_set<span class="op">$</span>small[[<span class="dv">4</span>]]</a></code></pre></div>
-<p>Size med:</p>
-<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb17-1" data-line-number="1">CP_models<span class="op">$</span>med <-<span class="st"> </span>CP_size_model_set<span class="op">$</span>med[[<span class="dv">4</span>]]</a></code></pre></div>
-<p>Size lrg:</p>
-<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb18-1" data-line-number="1">CP_models<span class="op">$</span>lrg <-<span class="st"> </span>CP_size_model_set<span class="op">$</span>lrg[[<span class="dv">2</span>]]</a></code></pre></div>
-</div>
-<div id="part-3-mortality-estimation" class="section level3">
-<h3>Part 3: Mortality Estimation</h3>
-<p>Estimating mortality requires bringing together models, carcass observation data (CO), and information on how the data was gathered. In particular the search schedule (SS) and proportion of carcasses in searchable areas (the density weighted proportion, or DWP), are needed. We will breifly inspect the files. Further information on the formatting of the CO, SS, and DWP files can be found in the User Guide.</p>
-<p>Carcass observations:</p>
-<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb19-1" data-line-number="1">data_CO <-<span class="st"> </span>solar_PV<span class="op">$</span>CO</a>
-<a class="sourceLine" id="cb19-2" data-line-number="2"><span class="kw">head</span>(data_CO)</a>
-<a class="sourceLine" id="cb19-3" data-line-number="3"><span class="co">#> carcID Unit Species Size Row Distance DateFound X Y</span></a>
-<a class="sourceLine" id="cb19-4" data-line-number="4"><span class="co">#> 1 x1 Unit82 SA small 28 13 2018-01-26 524641 3857110</span></a>
-<a class="sourceLine" id="cb19-5" data-line-number="5"><span class="co">#> 2 x2 Unit147 SA small 30 23 2018-01-13 525440 3857592</span></a>
-<a class="sourceLine" id="cb19-6" data-line-number="6"><span class="co">#> 3 x3 Unit235 SA small 11 44 2018-03-18 526724 3858148</span></a>
-<a class="sourceLine" id="cb19-7" data-line-number="7"><span class="co">#> 4 x4 Unit213 SA small 31 44 2018-02-04 526408 3858069</span></a>
-<a class="sourceLine" id="cb19-8" data-line-number="8"><span class="co">#> 5 x5 Unit204 SA small 34 10 2018-04-29 524953 3858081</span></a>
-<a class="sourceLine" id="cb19-9" data-line-number="9"><span class="co">#> 6 x6 Unit117 SA small 25 27 2018-04-06 527023 3857256</span></a></code></pre></div>
-<p>Search schedule:</p>
-<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb20-1" data-line-number="1">data_SS <-<span class="st"> </span>solar_PV<span class="op">$</span>SS</a>
-<a class="sourceLine" id="cb20-2" data-line-number="2">data_SS[<span class="dv">1</span><span class="op">:</span><span class="dv">5</span> , <span class="dv">1</span><span class="op">:</span><span class="dv">10</span>]</a>
-<a class="sourceLine" id="cb20-3" data-line-number="3"><span class="co">#> DateSearched Season Unit1 Unit2 Unit3 Unit4 Unit5 Unit6 Unit7 Unit8</span></a>
-<a class="sourceLine" id="cb20-4" data-line-number="4"><span class="co">#> 1 2017-12-20 winter 1 1 1 1 1 1 1 1</span></a>
-<a class="sourceLine" id="cb20-5" data-line-number="5"><span class="co">#> 2 2017-12-21 winter 1 1 1 1 1 1 1 1</span></a>
-<a class="sourceLine" id="cb20-6" data-line-number="6"><span class="co">#> 3 2017-12-22 winter 0 0 0 0 0 0 0 0</span></a>
-<a class="sourceLine" id="cb20-7" data-line-number="7"><span class="co">#> 4 2017-12-23 winter 0 0 0 0 0 0 0 0</span></a>
-<a class="sourceLine" id="cb20-8" data-line-number="8"><span class="co">#> 5 2017-12-24 winter 0 0 0 0 0 0 0 0</span></a></code></pre></div>
-<p>(Note that there are 300 arrays columns altogether: Unit1, …, Unit300)</p>
-<p>Density weighted proportion:</p>
-<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb21-1" data-line-number="1">data_DWP <-<span class="st"> </span>solar_PV<span class="op">$</span>DWP</a>
-<a class="sourceLine" id="cb21-2" data-line-number="2"><span class="kw">head</span>(data_DWP)</a>
-<a class="sourceLine" id="cb21-3" data-line-number="3"><span class="co">#> Unit small med lrg</span></a>
-<a class="sourceLine" id="cb21-4" data-line-number="4"><span class="co">#> 1 Unit1 1 1 1</span></a>
-<a class="sourceLine" id="cb21-5" data-line-number="5"><span class="co">#> 2 Unit2 1 1 1</span></a>
-<a class="sourceLine" id="cb21-6" data-line-number="6"><span class="co">#> 3 Unit3 1 1 1</span></a>
-<a class="sourceLine" id="cb21-7" data-line-number="7"><span class="co">#> 4 Unit4 1 1 1</span></a>
-<a class="sourceLine" id="cb21-8" data-line-number="8"><span class="co">#> 5 Unit5 1 1 1</span></a>
-<a class="sourceLine" id="cb21-9" data-line-number="9"><span class="co">#> 6 Unit6 1 1 1</span></a></code></pre></div>
-<p>These elements combine in the function <code>estM</code>, producing an object containing simulated arrival, detection, and mortality distributions. We also have the opportunity to provide the fraction of the facility being surveyed, <code>frac</code>, if it happens to be less than 100%. Increasing the number of simulations, <code>nsim</code>, will improve the accuracy of the estimates but comes at a cost of computer runtime.</p>
-<p>When estimating mortality, it is not currently possible to mix CP and SE models which differ in their dependence on size. Either both models depend on size class, or both models must be independent of size class. In this case we will choose here to use size dependence.</p>
-<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb22-1" data-line-number="1"> Mest <-<span class="st"> </span><span class="kw">estM</span>(</a>
-<a class="sourceLine" id="cb22-2" data-line-number="2"> <span class="dt">nsim =</span> <span class="dv">100</span>, <span class="dt">frac =</span> <span class="dv">1</span>, </a>
-<a class="sourceLine" id="cb22-3" data-line-number="3"> <span class="dt">data_CO =</span> data_CO, <span class="dt">data_SS =</span> data_SS, <span class="dt">data_DWP =</span> data_DWP,</a>
-<a class="sourceLine" id="cb22-4" data-line-number="4"> <span class="dt">model_SE =</span> SE_models, <span class="dt">model_CP =</span> CP_models,</a>
-<a class="sourceLine" id="cb22-5" data-line-number="5"> <span class="dt">unitCol =</span> <span class="st">"Unit"</span>, <span class="dt">sizeCol =</span> <span class="st">"Size"</span>,</a>
-<a class="sourceLine" id="cb22-6" data-line-number="6"> <span class="dt">COdate =</span> <span class="st">"DateFound"</span>, <span class="dt">SSdate =</span> <span class="st">"DateSearched"</span></a>
-<a class="sourceLine" id="cb22-7" data-line-number="7"> )</a></code></pre></div>
-<p>We are now able to get a confidence interval for estimated total mortality by taking summary of the estM object. Plotting it shows us us the estimated probability density for number of fatalities.</p>
-<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb23-1" data-line-number="1"><span class="kw">plot</span>(Mest)</a></code></pre></div>
-<p><img 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" style="display: block; margin: auto;" /> A point estimate for overall sitewide mortality is listed at the top of the plot, satisfying our first objective. The period of inference only covers the period over which we have have fatality monitoring data, which in this case is from 2017-12-20 to 2018-12-19.</p>
-</div>
-<div id="part-4-summaries" class="section level3">
-<h3>Part 4: Summaries</h3>
-<p>Having calculated the estimated arrival densities for each of the carcases, we can now use them to create a variety of summaries. Suppose that we are interested in how mortality changes with respect to three kinds of variables:</p>
-<ol style="list-style-type: decimal">
-<li>Temporally - by season, or finer resolutions</li>
-<li>Spatially - summaries by search unit</li>
-<li>Among Size classes, or other groups</li>
-</ol>
-<p>To create summaries, we split the data by differnt covariates, using a function called <code>calcSplits</code>. This requires the simulated mortality <code>$Mhat</code> and arrival times <code>$Aj</code> stored in the <code>estM</code> object, plus the search schedule and carcass observation data.</p>
-<p>Splits to the search schedule (splits in time) are specified by assigning a covariate to <code>split_SS</code>. These must be variables present in the Search Schedule file. To investigate differences in mortality between season, we will set <code>split_SS</code> to <code>Season</code>.</p>
-<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb24-1" data-line-number="1"><span class="kw">unique</span>(data_SS[, <span class="st">"Season"</span>])</a>
-<a class="sourceLine" id="cb24-2" data-line-number="2"><span class="co">#> [1] "winter" "spring" "summer" "fall"</span></a>
-<a class="sourceLine" id="cb24-3" data-line-number="3"></a>
-<a class="sourceLine" id="cb24-4" data-line-number="4">M_season <-<span class="st"> </span><span class="kw">calcSplits</span>(<span class="dt">M =</span> Mest,</a>
-<a class="sourceLine" id="cb24-5" data-line-number="5"> <span class="dt">split_SS =</span> <span class="st">"Season"</span>, <span class="dt">data_SS =</span> data_SS,</a>
-<a class="sourceLine" id="cb24-6" data-line-number="6"> <span class="dt">split_CO =</span> <span class="ot">NULL</span>, <span class="dt">data_CO =</span> data_CO</a>
-<a class="sourceLine" id="cb24-7" data-line-number="7">)</a></code></pre></div>
-<p>Splitting the estM creates a <code>splitFull</code> object, a plot of which shows boxplots for each season.</p>
-<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb25-1" data-line-number="1"><span class="kw">plot</span>(M_season)</a></code></pre></div>
-<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
-<p>Taking a summary of the <code>splitFull</code> object gives us a confidence interval for each level of the split covariate. The size of the confidence interval can be specified for both plots or summaries using the CL argument.</p>
-<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb26-1" data-line-number="1"><span class="kw">summary</span>(M_season, <span class="dt">CL =</span> <span class="fl">0.95</span>)</a>
-<a class="sourceLine" id="cb26-2" data-line-number="2"><span class="co">#> X 2.5% 25% 50% 75% 97.5%</span></a>
-<a class="sourceLine" id="cb26-3" data-line-number="3"><span class="co">#> winter 29.19714 40.91564 50.59319 56.62984 60.75414 67.83252</span></a>
-<a class="sourceLine" id="cb26-4" data-line-number="4"><span class="co">#> spring 84.65429 152.35914 174.55505 194.00978 204.95833 237.13772</span></a>
-<a class="sourceLine" id="cb26-5" data-line-number="5"><span class="co">#> summer 73.62571 128.99041 145.70543 156.55085 168.28860 189.29761</span></a>
-<a class="sourceLine" id="cb26-6" data-line-number="6"><span class="co">#> fall 45.52286 73.62254 89.98611 97.73037 107.37015 126.37458</span></a>
-<a class="sourceLine" id="cb26-7" data-line-number="7"><span class="co">#> attr(,"class")</span></a>
-<a class="sourceLine" id="cb26-8" data-line-number="8"><span class="co">#> [1] "splitSummary"</span></a>
-<a class="sourceLine" id="cb26-9" data-line-number="9"><span class="co">#> attr(,"CL")</span></a>
-<a class="sourceLine" id="cb26-10" data-line-number="10"><span class="co">#> [1] 0.95</span></a>
-<a class="sourceLine" id="cb26-11" data-line-number="11"><span class="co">#> attr(,"vars")</span></a>
-<a class="sourceLine" id="cb26-12" data-line-number="12"><span class="co">#> [1] "Season"</span></a>
-<a class="sourceLine" id="cb26-13" data-line-number="13"><span class="co">#> attr(,"type")</span></a>
-<a class="sourceLine" id="cb26-14" data-line-number="14"><span class="co">#> [1] "SS"</span></a>
-<a class="sourceLine" id="cb26-15" data-line-number="15"><span class="co">#> attr(,"times")</span></a>
-<a class="sourceLine" id="cb26-16" data-line-number="16"><span class="co">#> [1] 0 89 182 276 364</span></a></code></pre></div>
-<p>To get a finer summary of mortality, we need to parse the search schedule, using the function <code>prepSS</code>. This allows us to specify the exact time intervals over which we will split, in this case we will create a weekly summary.</p>
-<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb27-1" data-line-number="1"></a>
-<a class="sourceLine" id="cb27-2" data-line-number="2">SSdat <-<span class="st"> </span><span class="kw">prepSS</span>(data_SS) <span class="co"># Creates an object of type prepSS.</span></a>
-<a class="sourceLine" id="cb27-3" data-line-number="3">schedule <-<span class="st"> </span><span class="kw">seq</span>(<span class="dt">from =</span> <span class="dv">0</span>, <span class="dt">to =</span> <span class="kw">max</span>(SSdat<span class="op">$</span>days), <span class="dt">by =</span> <span class="dv">7</span>)</a>
-<a class="sourceLine" id="cb27-4" data-line-number="4"><span class="kw">tail</span>(schedule)</a>
-<a class="sourceLine" id="cb27-5" data-line-number="5"><span class="co">#> [1] 329 336 343 350 357 364</span></a></code></pre></div>
-<p>When we plot the splitFull object for a split with a custom schedule, we must specify that the rate is per split catagory by setting <code>rate = T</code>.</p>
-<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb28-1" data-line-number="1">M_week <-<span class="st"> </span><span class="kw">calcSplits</span>(<span class="dt">M =</span> Mest,</a>
-<a class="sourceLine" id="cb28-2" data-line-number="2"> <span class="dt">split_time =</span> schedule,</a>
-<a class="sourceLine" id="cb28-3" data-line-number="3"> <span class="dt">data_SS =</span> SSdat,</a>
-<a class="sourceLine" id="cb28-4" data-line-number="4"> <span class="dt">data_CO =</span> data_CO</a>
-<a class="sourceLine" id="cb28-5" data-line-number="5">)</a>
-<a class="sourceLine" id="cb28-6" data-line-number="6"><span class="kw">plot</span>(<span class="dt">x =</span> M_week, <span class="dt">rate =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
-<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
-<p>Next we will look at at splitting by covariates present in the Carcass Observation file. We specify a CO split by assigning split_CO to the name (or names) of the variables we wish to split on. Suppose we would like a summary of estimated mortality by unit.</p>
-<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb29-1" data-line-number="1"></a>
-<a class="sourceLine" id="cb29-2" data-line-number="2">M_unit <-<span class="st"> </span><span class="kw">calcSplits</span>(<span class="dt">M =</span> Mest,</a>
-<a class="sourceLine" id="cb29-3" data-line-number="3"> <span class="dt">split_CO =</span> <span class="st">"Unit"</span>,</a>
-<a class="sourceLine" id="cb29-4" data-line-number="4"> <span class="dt">data_CO =</span> data_CO,</a>
-<a class="sourceLine" id="cb29-5" data-line-number="5"> <span class="dt">data_SS =</span> data_SS</a>
-<a class="sourceLine" id="cb29-6" data-line-number="6">)</a>
-<a class="sourceLine" id="cb29-7" data-line-number="7"><span class="kw">plot</span>(M_unit, <span class="dt">rate =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
-<p><img 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" style="display: block; margin: auto;" /> There are 300 units in this example, each one gets a boxplot when we plot the splitFull. For those arrays which have at least one observation, we can create a summary. In this case we will only create a summary for arrays 8 and 100.</p>
-<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb30-1" data-line-number="1"><span class="kw">dim</span>(<span class="kw">summary</span>(M_unit)) <span class="co"># only 164 arrays had observations.</span></a>
-<a class="sourceLine" id="cb30-2" data-line-number="2"><span class="co">#> [1] 164 6</span></a>
-<a class="sourceLine" id="cb30-3" data-line-number="3"></a>
-<a class="sourceLine" id="cb30-4" data-line-number="4"><span class="co"># A list of the arrays without observed carcasses:</span></a>
-<a class="sourceLine" id="cb30-5" data-line-number="5"><span class="kw">setdiff</span>(<span class="kw">paste0</span>(<span class="st">"Unit"</span>, <span class="dv">1</span><span class="op">:</span><span class="dv">300</span>), data_CO<span class="op">$</span>Unit)</a>
-<a class="sourceLine" id="cb30-6" data-line-number="6"><span class="co">#> [1] "Unit2" "Unit3" "Unit4" "Unit5" "Unit9" "Unit13" "Unit17" </span></a>
-<a class="sourceLine" id="cb30-7" data-line-number="7"><span class="co">#> [8] "Unit19" "Unit20" "Unit24" "Unit25" "Unit28" "Unit29" "Unit34" </span></a>
-<a class="sourceLine" id="cb30-8" data-line-number="8"><span class="co">#> [15] "Unit35" "Unit36" "Unit37" "Unit38" "Unit39" "Unit40" "Unit41" </span></a>
-<a class="sourceLine" id="cb30-9" data-line-number="9"><span class="co">#> [22] "Unit42" "Unit45" "Unit47" "Unit48" "Unit51" "Unit55" "Unit56" </span></a>
-<a class="sourceLine" id="cb30-10" data-line-number="10"><span class="co">#> [29] "Unit57" "Unit59" "Unit60" "Unit62" "Unit63" "Unit66" "Unit69" </span></a>
-<a class="sourceLine" id="cb30-11" data-line-number="11"><span class="co">#> [36] "Unit70" "Unit71" "Unit73" "Unit74" "Unit75" "Unit81" "Unit84" </span></a>
-<a class="sourceLine" id="cb30-12" data-line-number="12"><span class="co">#> [43] "Unit85" "Unit86" "Unit88" "Unit90" "Unit91" "Unit93" "Unit94" </span></a>
-<a class="sourceLine" id="cb30-13" data-line-number="13"><span class="co">#> [50] "Unit95" "Unit97" "Unit99" "Unit102" "Unit103" "Unit104" "Unit105"</span></a>
-<a class="sourceLine" id="cb30-14" data-line-number="14"><span class="co">#> [57] "Unit108" "Unit109" "Unit111" "Unit119" "Unit120" "Unit121" "Unit122"</span></a>
-<a class="sourceLine" id="cb30-15" data-line-number="15"><span class="co">#> [64] "Unit124" "Unit125" "Unit127" "Unit128" "Unit132" "Unit133" "Unit136"</span></a>
-<a class="sourceLine" id="cb30-16" data-line-number="16"><span class="co">#> [71] "Unit143" "Unit146" "Unit148" "Unit149" "Unit151" "Unit164" "Unit166"</span></a>
-<a class="sourceLine" id="cb30-17" data-line-number="17"><span class="co">#> [78] "Unit169" "Unit172" "Unit175" "Unit179" "Unit181" "Unit185" "Unit186"</span></a>
-<a class="sourceLine" id="cb30-18" data-line-number="18"><span class="co">#> [85] "Unit188" "Unit189" "Unit191" "Unit192" "Unit193" "Unit197" "Unit199"</span></a>
-<a class="sourceLine" id="cb30-19" data-line-number="19"><span class="co">#> [92] "Unit200" "Unit201" "Unit202" "Unit205" "Unit209" "Unit211" "Unit216"</span></a>
-<a class="sourceLine" id="cb30-20" data-line-number="20"><span class="co">#> [99] "Unit221" "Unit222" "Unit225" "Unit226" "Unit228" "Unit231" "Unit233"</span></a>
-<a class="sourceLine" id="cb30-21" data-line-number="21"><span class="co">#> [106] "Unit234" "Unit236" "Unit246" "Unit247" "Unit249" "Unit251" "Unit254"</span></a>
-<a class="sourceLine" id="cb30-22" data-line-number="22"><span class="co">#> [113] "Unit255" "Unit256" "Unit257" "Unit259" "Unit261" "Unit262" "Unit264"</span></a>
-<a class="sourceLine" id="cb30-23" data-line-number="23"><span class="co">#> [120] "Unit266" "Unit267" "Unit272" "Unit274" "Unit275" "Unit280" "Unit282"</span></a>
-<a class="sourceLine" id="cb30-24" data-line-number="24"><span class="co">#> [127] "Unit283" "Unit285" "Unit288" "Unit289" "Unit291" "Unit293" "Unit294"</span></a>
-<a class="sourceLine" id="cb30-25" data-line-number="25"><span class="co">#> [134] "Unit295" "Unit297" "Unit298"</span></a>
-<a class="sourceLine" id="cb30-26" data-line-number="26"></a>
-<a class="sourceLine" id="cb30-27" data-line-number="27"><span class="co"># Create summaries for arrays Unit12 and Unit100.</span></a>
-<a class="sourceLine" id="cb30-28" data-line-number="28">whichRow <-<span class="st"> </span><span class="kw">rownames</span>(<span class="kw">summary</span>(M_unit)) <span class="op">%in%</span><span class="st"> </span><span class="kw">c</span>(<span class="st">"Unit12"</span>, <span class="st">"Unit100"</span>)</a>
-<a class="sourceLine" id="cb30-29" data-line-number="29"><span class="kw">summary</span>(M_unit)[whichRow, ]</a>
-<a class="sourceLine" id="cb30-30" data-line-number="30"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb30-31" data-line-number="31"><span class="co">#> Unit12 1 1 1.017586 2.165697 3.498534 5.401769</span></a>
-<a class="sourceLine" id="cb30-32" data-line-number="32"><span class="co">#> Unit100 3 3 4.591291 6.415282 8.473458 10.377703</span></a></code></pre></div>
-<p>It is possible to create summaries that split on both Carcass Observation variables and Search Schedule variables. To do so, include both a <code>split_SS</code> and a <code>split_CO</code> argument.</p>
-<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb31-1" data-line-number="1"></a>
-<a class="sourceLine" id="cb31-2" data-line-number="2">M_unit_and_species <-<span class="st"> </span><span class="kw">calcSplits</span>(<span class="dt">M =</span> Mest,</a>
-<a class="sourceLine" id="cb31-3" data-line-number="3"> <span class="dt">split_SS =</span> <span class="kw">c</span>(<span class="st">"Season"</span>),</a>
-<a class="sourceLine" id="cb31-4" data-line-number="4"> <span class="dt">split_CO =</span> <span class="kw">c</span>(<span class="st">"Size"</span>),</a>
-<a class="sourceLine" id="cb31-5" data-line-number="5"> <span class="dt">data_CO =</span> data_CO,</a>
-<a class="sourceLine" id="cb31-6" data-line-number="6"> <span class="dt">data_SS =</span> data_SS</a>
-<a class="sourceLine" id="cb31-7" data-line-number="7">)</a>
-<a class="sourceLine" id="cb31-8" data-line-number="8"><span class="kw">plot</span>(M_unit_and_species, <span class="dt">rate =</span> <span class="ot">FALSE</span>)</a></code></pre></div>
-<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAASAAAAHgCAMAAAAytUAGAAABBVBMVEUAAAAAADoAAGYAOjoAOmYAOpAAZpAAZrY6AAA6ADo6AGY6OgA6Ojo6OmY6OpA6ZmY6ZpA6ZrY6kJA6kLY6kNtmAABmADpmAGZmOgBmOjpmZgBmZjpmZmZmZpBmkGZmkJBmkLZmkNtmtrZmtttmtv+QOgCQOjqQOmaQZjqQZmaQkGaQkLaQtpCQtraQttuQ27aQ29uQ2/+2ZgC2Zjq2Zma2kDq2kGa2tma2tpC2tra2ttu225C227a229u22/+2/7a2/9u2///bkDrbkGbbtmbbtpDbtrbbttvb25Db27bb29vb2//b/7bb/9vb////tmb/tpD/25D/27b/29v//7b//9v///9S7Ag7AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAYPElEQVR4nO2dC3/bttWHKceemKRJk1XKZUvfpovUOlmbTfSS1XsXZ2bjrk5WaXIkfv+PMhxcSPB6QBEESen8f21sC8TBwaODC0ES9CJSpbyuHei7CBAiAoSIACEiQIgIECIChCgFaOXFmqQPu/kzT858mhM/LK+1P67jU9bKdn50KQsvLGA7Hy2qzJ36njd6cFHHhURmgEQNUUBlIOoByh2dACo2VA0oLPnODZUBVGLEsIZtAqowVAlo7XtfLdkxb/3KMCvV3gOK6xTuFkJVgD7cZW33+VJG6YQnM2f/deZ5D6PtKfuXpbEmzg7z7l3Eh0XR1RPPu/WC27h5yg77Na5XPjsvRRwcHr1/4h2dSiuJ3biJiQLW/sky5S0D9NczcWguLfel677FJWh1TXkUjP5aAUg2XlZiGtAf4K8Xc1kPFsJc7EtUgGTGh5FK/VIHlM6+ncelMEAs7eSNSNHsZgCpgAlU3LAP7saHZtKYndGLZa5SiW88m1bXlEcB+5kGpMQ82kwh5w0vMemkWfaji+gDy38R/QxGAt7Eb6ZJH7ryjt+xT554MziahckH30sAZbKH3ug1+1p9AZZXS1jR7GY7afE9rjXqYGQbQCnZNPYJjGFf/riUNYx900vQ66p7FHijSkCvVUIKELMtDG6mqm+QLVxWTXx7m+lYRXyoA0plV984P1D2EqmOJiwAtJnyeANLmlHhTjYNkt/y+Dq+SPuml6DVNeVR4E0qmhjE163nn7KAIL/oNiWg7fX5y6de7L8KUQjSlSCjf9np7KI+8hNZLXV0bDc3zIc8OONvR/0aQP5MmtQ1g8RbYOJbyvOkrimPGM8KQNszbur4Mg0IytYAbU+TaQY/bDNNwnCVjYls9sQd3odpgDS7OUDwQ4szxYPnz6QlgjDWfUt5ntQ15VE1IAD/kkXnuBIQa6bP/nz+KdQiKPn6CiKoDFA2gjS7OUDwgdaK4ghS0amlJd0AFJUKLd3zpK41IkhoDRnKAUl7LHaTPihxr6APSmfP9EEJIN1ufia9YsNgUlW9D8qmMW9eaL4EOjrN86Su6T6oCtDa593aB48D+t2yDBB0b2xYEP6Lw0bMp+0boMJGliWMCGWAMqOYBMSs6HZ1QL9bisrd8hNXxSh2MxelpNNg4HnI+pYtK2KW8k0vQa9rahTLAYrFacfTBJgyTMqamNRYHRadxc28aB6U6cL0eZACBFY0uzogeUoVeNrkWU2uZNyk0rRzMZj76L7pnmt1Tc2DKgHxGaUnzoJ/hiAo7qTPfBgAfuWN6WcRK3xaKubJNzBjLu+DotRMWsY/t6LZ1c7mZQHsr5Nk9ien5w+WqhZaWhRPmOU4nviW8lyra2omvRjoelBQcWJVlVZfwwR05WcnOmZpO2iIgAKvfHGnKm0nDRHQG2/07S5pO2mIgJyKACEiQIgIEKLDBfTR7LA8ILXKk11TWbETr0eli+PrO5dyls7XKubJokuFVrIIWIVQ2WCMZmcHsHBxH8vueb+XHq2M5j6a/9t59WQgPrQA0KO7MONff3EnB6jCIABa85PEgJ9hzQycUCa383H8/2bKTh1vLyAlmFXnjgJtXbXu5LARoFdQcPjNHXEqCWHAvuLRKxFB/PRzxo76wddPChkgVWYwloCilRZC2XzSZFw7RoX9x/MwW/BfEkAsr1pOZz+VCTYnPHrPPFKWMsmZXLLWMhU+HKuk7XevvNHdsXA99pNfhCg6F9s8+stjVtr3/y+rHMLZ4kSsbbACpjMeJOJMPgEgKpWwygLK5lMmIS0U38GMHw6GeASdLbS8cDgEGGSNi2bVYR4pS8pZlRznmsgSBCCt/KR6LHnFF7RnsZ+PeBSvjooALVhwrx8yT1fi1HvGf2p9EPwKhrTAhqOT70kAguaSkpZPmUwiyJ/xakAYQR+0fqhnk0VEcR14G+SAlCXlbKoEmVl+YeoLXgmcKomDggRVn1UMqLiJLcJJFE6YP3IpZRLKr5YDgh4VB6QWWjS7qXzKpEjhKzIJINDZQnTVUfz9r0SDmWQAKUvK2VQJ8XLQpACQShL4mLVgnPgpKjErBrS+/9t3CyhTVlkDtIHLSMURlG1igd5xZvPpgLhPL+ZxE+M0xuDHnaQJ4oDU95MBpPWDWUAySQBaHb1nabGfvLFkr4vFAfDdD/eXqolFEoSMOzBaDIhH6mqcdNI6oWw+vYmlCIs43PIvSDbpGk1MA6Q3sWJAKkl1CDAsxX6KolkUFQHiF9N4laH3gj5/rDppMLr2R4sCQGKYD8T1J1FkehzW8ymTPO/tRaR6YdlvrXjd4yY4i8Kx1kmnAClLylmVHOcSHxcASqrHmzK/tBD7Kat6uxgQuLyWw7xcp1XDPB9c9a9xEgNS88O4SLjqI9Nz+aRJQU92EmqiCAHEP5UzITb0Hl0mw3waUGxJOhsna7nkfEQDxHHIJOktGyY0P0VVvVnjU411fvnlv4vq9EGpMaCfkduSsPS+63BPVg1FgBARIEQECBEBQkSAEBEgRAQIEQFCRIAQESBEBAgRAUJkB5DXe3UNyIqVFkWAEBEgRHsPqGFHsv+AmpZBgFrLTIDaLNaylRbLIECtZSZAVnOKG7OYwvSdPwRIKpQ3bqx9AlQqFjzb7/6oADU9FzQXXkaFLw4BbR5fMkZ9bWJlB7kDxG+Oub8kQCXidxDGm5hYKN9cgwCkbirs7SjWNaBAxQ4B6sKKhTII0I4HESDkIAKEHESAkIMIEHIQAUIOIkDIQfsNqMYl5K4BJU8ypjcJaxmQeUFdAxILZmH8UGrz8k00IEAgcRqmTsZcLJgNCxAsmKl/LZRvokEBEk/iZXcYIEBK4pFb9eCthfJNNCBAYsFsndmTgQDFEgtmgeMl1wEBateKgXUChFgnQIh1AoRYJ0CIdQKEWCdAiHUChFgnQIj1vgOSS2XJtiONyzdZLxnQiqJYKks2rmlevkFus8d2qo9xu2C2vr/k+9dofu1cvokPRhFU3Q7VR8l7IzJtYGfncto8vlzBRkcWl1xrtJumgOTO7ytvltuBbkfnsoKlsgED4rtYRXxnM31zswbOZcSXyvQmtpOVmj5YBMQ3oIpSe541dC4t0Tc77qTtRpBoWBBBbQCSS2WGw7xpB95JH6QaW1PnzFQ++NrwwSYgMY7BDnGG2+MeHKC6OjBA6A7OVXYbaDCA1ChmrgMDVH9n7gMDFJ9qGHM6MED1RYDMC2+glgHZO5uvr9onq3DfS2h8h5kVQEZHGQDS1jpa64P4FtBwsmp4h1mvAO2kejm3379/fBnVWDCzBchgXbbSnuMmts6+u91FBJkb6RzQ6sSgiVlYaa+lPgEKxeb+1VaMxubdfMCMdA4I3hwQYDdQHTIgWDdDh3nngHpz2cfQihmgGj0V4kH1xbPhArLmyb5GkDVP+tIHGVshQIgVAoRYIUCIFQKEWCFAiJVDBWS8YHaggMwXzA4TkFgw0/fGMZm7HhAg0cSCb3xqYmXigGC5g5pYsTigWXYLKgIUiy+YmdyjeMiAYJhH9zAzW+nZP0CmVpAFrIKMBwaIIgizYtgH2Vpy3VNAhg2xpgt7BIgiCCvDYh9Ei/Y7OtLQOAHa0WJjK4cKSO6shO8GfKCA+IKZ0ab/hwlILJgZbfrfAaCK6YLrk1WDTf+7iSCDclrOuRH3KBKgMvE7zCCQ+7ZgVi0axRARIER9nCiarQdZO1mtVg8B1TDfKpqmRRCg1nLiVoyajoP2FfUVUI9EgBARIESdA+q9Oga0xyJAiAgQIgKEiAAholHMGNDnj5o+7Wqlp7IACDbZTFRzj5RDABSF3uhZrK+NttCzUL4jWWlip75377Xr8od1Nn996jFG9YKnYfnNc7dcRDbn1RPPO67N6IAAsd767V3PM9vG00L5zXO3XERBzs+MkNNRbFCAPr/1PW/0zYWr8pvnbrmIVE7eum49rzlLbFR+89wtF5Hk5HTq988Ny2+eu+Ui9Ini8Y/dnGoMA1CHpxoDAfS3Z886OtUYBqCOyrfnQ0tFEKDWctqzsl+A+AZmFt/M0jh3y0XUzQk3ALve9N+CnAHiNwC7fm2EBTkDxG8A1h/qtbHeNRBAJov24gZg129msSALgPStcktn0uIGYItNzPTKTNM4tTGT/uWc6dR78OP56ej5P0pn0qHVTtor/BU7tFE5jXKK95awQCl/d4DdYX5ggOSzPPHP9ssnQOYZhwBoOxdNJ3S2aD8wQNC5PDj/6dTL7KfdXvlDAxStn8BwelyTzwEBYvPF63f1l6UPCZBbKwME9Murr3/7t7PyBwfog89OM95Paw5ihwNo5R3/k82BAq/ma8r2CVCmCWXmQaMFTBLX/sHOg3JNKDeTVv+3U35Fxl4AyjehrgGZ34iKl1Fhx9DBgiaUzhnAO5CPLlee2TtI65afz1fjVl2jMsoOMnSwIELSOdf+6Et/9H/ZV/fs7Bee0YSPhTCzBaifpxoWOipTB/NNKJdze/3O4f1BTgEZvIAt34TSnfTTezzfdt6rUcxmBMl3PZf2IbkmpFn8+PF6evQOrmlc+fsKSHvffJmJTBNKLKYua/RqomgRkPa+edS3nMWb85/80Y9wbeO8/KJGPb/qZHQUQWIGGBRHwPaluDFK9TVZiyq9XAG/nuH4qobdPgg8D0v6INaKOLnyYR7Ragzbv7i+LmZ1mOc9SVkPAonHl5WAPvIm9tPXpU2UAarxlkxMPZsHMTB/9xmhipk0dhMnXFO0eG2+f4DYedjRRSmgwLvHZkpPK69qBLNBA+LdTFCy3iVeG+If/bME0GY6WgQMTli1YBZavXnBxIbVTnq0YHOgzbS4giJyoB2VATq6hDZUumAmYmfAnTQM8zBJDIv7kHgfzQpAMBcvXw8K+PZ3hsO8yW0tmA3jg6rT9YkiACqbKH4Wk+ib5EbxTB80Wqz9MbRCS6caNSrkNIJKJoqoxZV39K+59/u5tQWzvgGSfVDR/T1iHSh3tp+x+OHOJZzPHtcLoAEBEhPFolEaTiO2L3PPYhRZ/Fx7QWh3QNZWFKsP2nkUaZDTyIqReeQgs4Xr6jCzBujK8tM+NgAZRlClvfgj1csUj0L/hmozBtr2AWljIbIiaeCdYUKdgyx0VMkoVjF8feCD9xnU/2GxMYa35jXncpeQhDoHWQSkFsyKtPZH37IZkjde3kyTUS43UUScRb0zTKhzkNUIKo+AgFOBUy1GqfjCIVxYRJxFvTNMqHOQRUDR+nZZDUXlRRusWO4Y233quX+A/LJOVp2ojqOqBbMr3/sCdG9vO+nScwQBRSzJaudauVFsZAmQhevFBektdtLiHmjeBTEOxVdW2Sj2AnEW9a7gox4BquqkV97xuyvewlg7bH8U6yegik5azIAYgM28dB4kA6y+BgMoPl0vjIRrfkFwMz0unUmzQa7mvi+oS8WpWO76ppycrG6efmHtVMMxIOSM1h4gKQujWGUqlru+qcEtd/SzD6qvejlhi5ix2aL93gEqWZJNKxwzRmaXffYOUMmSbF7pC4cmrb4xIIsLZrVVO+fm8aXFt4XX8AE5ygkgg3sUWfMyfJ26vXMxo6McADK5R5HP1E2uzZuttJfl3uGo9gGZ3KMo+mazTnr/IsjgHsUAqjtxPcwbHeWkD8LvUTSxkvtofwDZtGIKyKwVYlacArp6+qDuKX2zCNq5jFSyk3MxGN9ZB2NjYwHXgKoPsgUo8E5+m3sTG8+L7SUguDS09sVDd7tbyX20P4DELXh2HsncW0BwxcPGU897CYjfJ83mgAH1QSU5t/yKx2Zq4Ra8/QQURZ9Z29p+bGol2l9A1qzYBdR0um0B0PaX5Ay19jmZiwja1ZGGLsQ5xdAulsx6Oczv6khDF7KAtH+b+UWAkPJrLJhZ1IAANXVsNxEgRASo0ovyBk2AENkB9E5d+LkuB7R5fFnnufm9AqSPO2WAeGzVeCRzjwCJDbeVSq77bL9/zyKoxkO9ewTIUNDEamz6T4AQK4cJyLSJOZ9Hl8stINtvZnEgt4Bsv9vHgXq4YNYvdQ6o9+oY0B6LACEiQIgIECIChIhGMTeArFhpUQQIEQFCNGBATfsIw1I6yGnPioP4I0CtFUGAWstpzwoBcuJDS0UQoNZy2rNCgJz40FIRBMhaTn49I+CX7S1f1dgPQPy2BXG90PZ1sb0AJG9b4FxsbvpvY8nGpBwHOflV+Vu+N3a+6b8FOeuDPi+j0Pmm/xbkDFDUxab/FuSsibHYCZ1v+m9BLod5803/6xU/eECtWCFA5hkJEJKRACEZCVBhxj2aSbdhxdLlYYOCOshpxYopn6YEhwvI3EYjTwlQa5kJUJvFNrdCgMwzEiAkIwFCMhIgJCMBQjISICQjASrMaH4q1hyQ/tzyLu+bb1p+a7ktnNAONoKMcltYNCJADYtoI6c9K64A7W8f1LwgiqCGRdjIqT3v7Pj+IJujWPV7VndwLpa2rYnj+4NsLFzH2YKTZQh7sZa/TbSec7H0bU0s3h9k5IPFCNpMJ9HqZMlfqW7HuUTJrh0Wb38xym2xD4J3HEL0Z1qAWeGI9gIQvOMQ3p3SFqB8E6trZRcfbI5iIetJg0lrTayTTtruMB+MYSQz36NtEMN884JootiwiDZy2rPiElB7E8UWrTgdxWru1j8MQDYnisZT6EzOZmoXkIUy9HnQbjmbaTCAKt/1bL9YS1acAvL3sJO2UEbSxGq+MeTQAFEnjWSmThrLTJ10deb4wsZBdtIVc8n9PhercVTZQQMGZHwKYQcQLEqHpS/a3bVYR1YslIECCk6Wa38My2Y2i3VkxUIZGCCYB6089v+gOmmLZZgAChickACVfB6MN9OT5WZKTazkczYRGi3qnJHtDaDqVbUBD/O2yqhelyVABGgggFrZP8hMgwDUzv5BBjJ9aLPrTtr6/kHWhNxl5QqQ9f2D7KkfEWR9/yB76kcfBLK6f5A99QOQ9f2D7KkfgKzvH2RPPQHUqpVmLvSjk27VigXt4Zq0XREgRAQIEQFCRIAQESBEBAgRAUI0LECu14qGeHdHX0KLACHqISALt8dbVB8B2S6jkfoIiCKo0gpylcG1+geIIgizQn0QYoUiyMhKD9hw9RNQb+Knr4B6JAKEiAAh6hxQ79UxoGbmzXywaKqOCJBzi/XNEyArPhAgd6bqiAA5t1jf/CEDGr4IECIChIgAIWoFUGoLkVXd7UT6pdYjqP5+K/0SAUJkDRB/UBYeSY+Ck/8wKJvpn6aeN4Mdn2D3XY/fgb559Kp6WxG+QdRMUGX/rH0wMoFPZ1Hqj8jYZEPZi6CQcxjDFj0bDmi0iMLRgteV/WT1m0TwSHaVDb7PM6OcAAIjnnjQPfWHscmmsgdoxTwOnp0s17cXAtCEVzj+HXY7EL9U2JCxkADiRoSl1B/GJpvKHiBWn82jH+5ccqeTVsI3gxjBs0GSVrUNQSgBNJNhxQFpfxibbCqLnXQwWd//z6MF3243A0iue+K12c4zfVApIGOTDWURUHjyZhwFX81neUAj+XSZUW0C2XEZRJCpyQayCGh95w+TKLx1e5EDFFfCqDY80yTPJPVHPZMNZBEQH7dWMKinAU3EKBZHRoV4XKxgc42TJWttFYCMTTaVzYki7DvDRxkdUBSoeRC8dgKrDXQtUHPYjO1PVU0sMjbZUHSyiogAISJAiAgQIgKEiAAhIkCICBAiAoSIACEiQIgIECIChIgAISJAiAgQIgKEiAAhIkCICBAiAoSIACEiQIgIECIChIgAISJAiAgQIgKEiAAhIkCICBCizgBdPfU8797rroo3VleAzuRdqg87Kt9YHQFa+yMInl+feO3eQNdcHQEKJRh4IWO/1RGgVSpybk49b/ScP3Lx4a78dXvmsz7qQiSzXx9cwG3mJyzmRt+69LQjQNu59+C1eghFvgMeYikUPdOEv6BC3vEqk48uxX34ItmZuuqkt6dePIoF3ldLFjH8EYQjFihr/ppGeIjng0D1kCfDg0TeWDxS5E7dzYO2v7y8y58lk/2QfDPjx3P2MQAaPTj/BB/I552286NL+D/+wJG6nShu37BokE1IwlK/8cYGvRG/NT3id+vDDfi8J9p7QHEQQEzEgOA5Ku/L5/+45qnXpwLVQQKChyz4TwEo7nTF4Bbj+3z1BB7G0JrYwQBaibH8Zs673tG30CGzrmgFnfDNEx4340+QDo8xap30wQBS47l3fBmP46MFf4ZFDulimIcBK0k+JEDR9VNW71tidggTRT4ThOjxbr2ABrh9C+kvVPLo4afosAANRQQIEQFCRIAQESBEBAgRAUL0P33ZQwx1/PVQAAAAAElFTkSuQmCC" style="display: block; margin: auto;" /></p>
-<p>Two CO variables can be compared simultaneously by specifying an ordered pair of covariates for <code>split_CO</code>, however currently there are a limited total number (two) of splits which can be allocated among temporal or carcass covariates.</p>
-</div>
-
-
-
-<!-- dynamically load mathjax for compatibility with self-contained -->
-<script>
- (function () {
- var script = document.createElement("script");
- script.type = "text/javascript";
- script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
- document.getElementsByTagName("head")[0].appendChild(script);
- })();
-</script>
-
-</body>
-</html>
diff --git a/inst/doc/wind-examples.R b/inst/doc/wind-examples.R
deleted file mode 100644
index 544d2cc87fb367d7349f4a5b31f364a5ca8bc61c..0000000000000000000000000000000000000000
--- a/inst/doc/wind-examples.R
+++ /dev/null
@@ -1,197 +0,0 @@
-## ----setup, include = FALSE----------------------------------------------
-knitr::opts_chunk$set(
- collapse = TRUE,
- comment = "#>"
-)
-
-## ----set-options, echo=FALSE, cache=FALSE-----------------------------------------------------------------------------
-options(width = 120)
-
-## ---- include=FALSE---------------------------------------------------------------------------------------------------
-library(GenEst)
-vers <- packageVersion("GenEst")
-today <- Sys.Date()
-
-## ---------------------------------------------------------------------------------------------------------------------
-data(wind_RPbat)
-names(wind_RPbat)
-
-## ---------------------------------------------------------------------------------------------------------------------
-data_SE <- wind_RPbat$SE
-data_CP <- wind_RPbat$CP
-data_SS <- wind_RPbat$SS
-data_DWP <- wind_RPbat$DWP
-data_CO <- wind_RPbat$CO
-
-## ---- include = FALSE-------------------------------------------------------------------------------------------------
-daterange <- range(data_SS$SearchDate)
-seasons <- paste(unique(data_SS$Season), collapse = ', ')
-
-## ---------------------------------------------------------------------------------------------------------------------
-head(data_SE)
-
-## ---------------------------------------------------------------------------------------------------------------------
-head(data_CP)
-
-## ---------------------------------------------------------------------------------------------------------------------
-head(data_SS[, 1:10])
-
-## ---------------------------------------------------------------------------------------------------------------------
-head(data_DWP)
-
-## ---------------------------------------------------------------------------------------------------------------------
-head(data_CO)
-
-## ---------------------------------------------------------------------------------------------------------------------
-model_SE <- pkm(p ~ Season, k ~ 1, data = data_SE)
-class(model_SE)
-model_SE
-
-## ---------------------------------------------------------------------------------------------------------------------
-model_CP <- cpm(l ~ Season, s ~ Season, data = data_CP, dist = "weibull",
- left = "LastPresent", right = "FirstAbsent")
-class(model_CP)
-model_CP
-
-## ---- fig.width = 7, fig.height = 5, fig.align = 'center'-------------------------------------------------------------
-Mhat <- estM(nsim = 1000, data_CO = data_CO, data_SS = data_SS,
- data_DWP = data_DWP, model_SE = model_SE, model_CP = model_CP,
- unitCol = "Turbine", COdate = "DateFound")
-
-summary(Mhat)
-plot(Mhat)
-
-## ---- fig.width = 5, fig.height = 5, fig.align = 'center'-------------------------------------------------------------
-M_species <- calcSplits(M = Mhat, split_CO = "Species", data_CO = data_CO)
-summary(M_species)
-plot(M_species)
-
-## ---------------------------------------------------------------------------------------------------------------------
-SSdat <- prepSS(data_SS)
-
-## ----Season Split, fig.width = 6.5, fig.height = 5.25, fig.align = 'center'-------------------------------------------
-M_season <- calcSplits(M = Mhat, split_SS = "Season", data_SS = SSdat,
- split_CO = NULL, data_CO = data_CO)
-summary(M_season)
-plot(M_season)
-
-## ----Temporal Split, fig.width = 7, fig.height = 5, fig.align = 'center'----------------------------------------------
-M_month <- calcSplits(M = Mhat, split_time = seq(0, max(SSdat$days), by = 28),
- data_SS = SSdat, data_CO = data_CO)
-summary(M_month)
-plot(M_month)
-
-## ----Time unit Split, fig.width = 7, fig.height = 5, fig.align = 'center'---------------------------------------------
-M_various_times <- calcSplits(M = Mhat,
- split_time = c(seq(0, 90, by = 15), 120, 150, seq(155, 200, by = 5)),
- data_SS = SSdat, data_CO = data_CO)
-plot(M_various_times)
-plot(M_various_times, rate = TRUE)
-
-## ----Species and Season, fig.width = 4, fig.height = 6, fig.align = 'center'------------------------------------------
-M_species_by_season <- calcSplits(M = Mhat,
- split_CO = "Species", data_CO = data_CO,
- split_SS = "Season", data_SS = SSdat)
-plot(M_species_by_season)
-
-## ---------------------------------------------------------------------------------------------------------------------
-data(wind_cleared)
-names(wind_cleared)
-
-## ---------------------------------------------------------------------------------------------------------------------
-data_SE <- wind_cleared$SE
-data_CP <- wind_cleared$CP
-data_SS <- wind_cleared$SS
-data_DWP <- wind_cleared$DWP
-data_CO <- wind_cleared$CO
-
-## ---------------------------------------------------------------------------------------------------------------------
-head(data_SE)
-head(data_CP)
-
-## ---------------------------------------------------------------------------------------------------------------------
-pkModels <- pkm(p ~ Visibility * Season, k ~ Visibility * Season, data = data_SE,
- allCombos = TRUE, sizeCol = "Size")
-class(pkModels)
-names(pkModels)
-
-## ---------------------------------------------------------------------------------------------------------------------
-names(pkModels[["sml"]])
-class(pkModels[["sml"]])
-
-## ---------------------------------------------------------------------------------------------------------------------
-aicc(pkModels[["sml"]])
-
-## ---- fig.show = "hold", fig.width = 7, fig.height = 7----------------------------------------------------------------
-plot(pkModels[["sml"]], specificModel = "p ~ Visibility; k ~ 1")
-
-## ---- fig.show = "hold", fig.width = 7, fig.height = 7----------------------------------------------------------------
-plot(pkModels[["sml"]], specificModel = "p ~ Season; k ~ Visibility * Season")
-
-## ---------------------------------------------------------------------------------------------------------------------
-pkMods <- list(
- sml = pkModels[["sml"]][["p ~ Visibility; k ~ 1"]],
- med = pkModels[["med"]][["p ~ Visibility; k ~ 1"]],
- lrg = pkModels[["lrg"]][["p ~ Visibility; k ~ 1"]],
- bat = pkModels[["bat"]][["p ~ Visibility; k ~ 1"]]
-)
-
-## ---------------------------------------------------------------------------------------------------------------------
-cpModels <- cpm(
- l ~ Visibility * Season, s ~ Visibility * Season,
- data = data_CP, left = "LastPresent", right = "FirstAbsent",
- dist = c( "weibull", "lognormal", "loglogistic", "exponential"),
- allCombos = TRUE, sizeCol = "Size"
-)
-
-## ---------------------------------------------------------------------------------------------------------------------
-lapply(aicc(cpModels), nrow)
-aicc(cpModels[["sml"]])
-
-## ---------------------------------------------------------------------------------------------------------------------
-cp_smlCandidates <-
- names(cpModels[["sml"]])[c(25, 24, 15, 19, 20, 10, 14, 22, 74, 21)]
-cp_smlCandidates
-
-## ---- eval = F--------------------------------------------------------------------------------------------------------
-# plot(cpModels[["sml"]], specificModel = cp_smlCandidates)
-
-## ---------------------------------------------------------------------------------------------------------------------
-cp_sml <- cpModels[["sml"]][[cp_smlCandidates[1]]]
-
-## ---------------------------------------------------------------------------------------------------------------------
-cp_med <- cpModels[["med"]][["dist: weibull; l ~ Visibility; s ~ Season"]]
-cp_lrg <- cpModels[["lrg"]][["dist: exponential; l ~ Visibility + Season; NULL"]]
-cp_bat <- cpModels[["bat"]][["dist: weibull; l ~ Visibility + Season; s ~ 1"]]
-
-## ---------------------------------------------------------------------------------------------------------------------
-cpMods <- list(
- sml = cp_sml,
- med = cp_med,
- lrg = cp_lrg,
- bat = cp_bat
-)
-
-## ----Mhat plot, fig.height = 4, fig.width = 7, fig.align = 'center'---------------------------------------------------
-Mhat <- estM(nsim = 1000, data_CO = data_CO, data_SS = data_SS, frac = 0.23,
- data_DWP = data_DWP, model_SE = pkMods, model_CP = cpMods,
- sizeCol = "Size", unitCol = "Turbine", COdate = "DateFound")
-
-summary(Mhat)
-plot(Mhat)
-
-## ----Species Group Plot, fig.height = 5, fig.width = 5, fig.align = 'center'------------------------------------------
-M_speciesGroup <- calcSplits(M = Mhat,
- split_CO = "SpeciesGroup", data_CO = data_CO)
-summary(M_speciesGroup)
-plot(M_speciesGroup)
-
-## ----Split Species and Season, fig.height = 12, fig.width = 4, fig.align = 'center'-----------------------------------
-M_speciesseason <- calcSplits(M = Mhat,
- split_CO = "Species", data_CO = data_CO, split_SS = "Season", data_SS = data_SS)
-summary(M_speciesseason)
-plot(M_speciesseason)
-
-## ----Transposed Species Season, fig.height = 7, fig.width = 7, fig.align = 'center'-----------------------------------
-plot(transposeSplits(M_speciesseason))
-
diff --git a/inst/doc/wind-examples.Rmd b/inst/doc/wind-examples.Rmd
deleted file mode 100644
index d4324b4fed9bde3c231913f1e954333a563e6a8e..0000000000000000000000000000000000000000
--- a/inst/doc/wind-examples.Rmd
+++ /dev/null
@@ -1,669 +0,0 @@
----
-title: "GenEst - A Tutorial with Wind Examples"
-author: "Daniel Dalthorp"
-date: "`r Sys.Date()`"
-output: rmarkdown::html_vignette
-vignette: >
- %\VignetteIndexEntry{GenEst - 1. A Tutorial with Wind Examples}
- %\VignetteEngine{knitr::rmarkdown}
- \usepackage[utf8]{inputenc}
----
-```{r setup, include = FALSE}
-knitr::opts_chunk$set(
- collapse = TRUE,
- comment = "#>"
-)
-```
-```{r set-options, echo=FALSE, cache=FALSE}
-options(width = 120)
-```
-```
-
-```{r, include=FALSE}
-library(GenEst)
-vers <- packageVersion("GenEst")
-today <- Sys.Date()
-```
-## Introduction: Tutorial with Examples
-This tutorial provides an introduction to the array of command line tools
-**GenEst** provides for estimating carcass arrivals and detection
-probabilities. The approach is to walk through analyses of realistic but
-fictitious data sets representing studies of bird and bat mortality at a
-wind power facility.
-
-The general steps in the analysis are:
-
-1. Construct a model for Searcher Efficiency
-2. Construct a model for Carcass Persistance
-3. Estimate mortality
-4. Specify the type of summary desired (e.g., mortality by species and season)
-
-Data required for a full analysis include results of searcher efficiency trials,
-results of carcass persistence trials, search schedules for all units searched,
-the search coverage within each unit, and results of periodic carcass surveys.
-More information about the kinds of data required can be found in the User Guide.
-For convenience, data required in this tutorial are available in R as packaged
-lists, which can readily be loaded as described below. Alternatively, the data
-may be downloaded into zipped .csv files from the GUI.
-
-To perform the analyses illustrated in the tutorial, begin by starting R and
-loading GenEst from the command line: `library(GenEst)`
-
-## Example 1: Estimating Bat Mortality from Searches on Roads and Pads
-Searcher efficiency and carcass persistence would be expected to vary with
-carcass size (sparrow, eagle, bat), ground characteristics (road & pad,
-cleared field, vegetation type), season, etc. In this first example, we limit
-the analysis to one carcass size (`bat`) and one ground visibility class
-(`RP` = road and pad). A more complicated scenario is analyzed in example 2.
-
-The required data is stored in `wind_RPbat`, a list of data frames with results
-for searcher efficiency (`SE`) and carcass persistence trials (`CP`), search
-schedules for all turbines (`SS`), the search coverage or density weighted
-proportion (`DWP`) of area searched at each turbine (i.e., the fraction of
-carcasses expected to fall in the search plots), and carcass observation (`CO`)
-data.
-
-Load the full data set into R:
-```{r}
-data(wind_RPbat)
-names(wind_RPbat)
-```
-To streamline the notation, extract the data from the `wind_RPbat` list into its
-components:
-```{r}
-data_SE <- wind_RPbat$SE
-data_CP <- wind_RPbat$CP
-data_SS <- wind_RPbat$SS
-data_DWP <- wind_RPbat$DWP
-data_CO <- wind_RPbat$CO
-```
-
-```{r, include = FALSE}
-daterange <- range(data_SS$SearchDate)
-seasons <- paste(unique(data_SS$Season), collapse = ', ')
-```
-### Searcher Efficiency (`SE`)
-Searcher efficiency trials were conducted on roads and pads, with a total of
-`r dim(data_SE)[1]` fresh carcasses placed in the field over the course of
-the entire monitoring period, evenly divided among seasons (`r seasons`).
-Carcasses that were later discovered by search teams during the course of
-normal carcass surveys were removed from the field. Carcasses were left in
-the field for up to 5 searches after carcass placement.
-
-Results of the SE field trials are stored in the `data_SE` data frame:
-```{r}
-head(data_SE)
-```
-Columns `s1, s2, ..., s5` show the fate of carcass `pkID` on the 1st, 2nd, ...
-5th searches after the carcass was placed. A 1 indicates that the carcass was
-discovered, a 0 indicates that the carcass was present but not discovered, and
-NA indicates that the carcass was no longer present for discovery or no search
-was conducted.
-
-### Carcass Persistence (`CP`)
-Carcass persistence trials were conducted on roads and pads, with a total of
-`r dim(data_CP)[1]` fresh carcasses placed in the field over the course of
-the entire monitoring period, evenly divided among seasons (`r seasons`).
-Carcasses were checked approximately 1, 2, 3, 4, 7, 10, 14, 21, and 28
-days after placement in the field. Exact times were entered as decimal
-fractions of days after placement.
-
-Results of the SE field trials are stored in the `data_CP` data frame:
-```{r}
-head(data_CP)
-```
-
-Exact persistence times are not known, but a carcass that was present at one
-check and absent at the next check is assumed to have been removed at some
-point in the interval. The left endpoint of the interval was entered as `LastPresent`
-and the right endpoint as `FirstAbsent`. For carcasses that had not been scavenged
-by the end of the study, `LastPresent` is the time of the last check and `FirstAbsent`
-is `Inf`. For carcasses whose removal time is known exactly (e.g., scavenging
-was recorded by camera), `LastPresent = FirstAbsent`. The `Season` column gives the season at
-the time the carcass was placed in the field.
-
-### Search Schedules (`SS`)
-Carcass searches were conducted on roads and pads within a 120 m radius from
-all `r dim(data_DWP)[1]` turbines at a fictitious wind power
-facility. Monitoring began on `r daterange[1]` and continued through
-`r daterange[2]`. Searches spanned `r length(unique(data_SS$Season))` seasons:
-`r seasons`. Search intervals varied by turbine and by time of year, ranging
-from daily searches at some turbines in the fall and searches once every 12
-days in the spring at some other turbines. Search schedules for all turbines
-are stored in `data_SS`, which is a data frame with a column for search dates
-(including all dates that any turbine was searched); a column of 0s and 1s for
-each turbine, indication whether it was searched on the given date; and zero
-or more optional columns giving additional information about the date (e.g.,
-season).
-
-```{r}
-head(data_SS[, 1:10])
-```
-
-Note that we have only displayed a few of the turbine columns - there are 100
-turbine columns altogether (t1, ..., t100).
-
-
-### Density Weighted Proportion (`DWP`)
-The density-weighted proportion (`DWP`) is the expected fraction of carcasses
-that fell in the searched area. Carcass density is not the same at all distances
-from a turbine, but typically rises over a short distance then decreases
-eventually to 0. Searches were conducted on roads and pads within a 120 m
-radius from all 100 turbines to provide sufficient data with which to model the
-change in density with distance and from this, accurately calculate the fraction
-of all carcasses that are expected to land on road and pad surrounding each
-turbine (density-weighted proportion or DWP). The configuration of the
-roads and pads differs among turbines, hence the DWP must be calculated for each
-turbine. DWPs for bats at each turbine are stored in `data_DWP`, which is a data
-frame with a column for turbine name (note that turbine names also must be
-included among column names in `data_SS`, which gives the search schedule at
-each turbine) and a column of DWP labeled `bat`. In other studies where, for example,
-mortality of birds might be of interest, `DWP` would be expected vary with
-carcass size or species (e.g., the spatial distributions of bats and large birds
-around a turbine would likely differ from one another). In that case, each
-carcass size class (`large`, `medium`, `small`, `bat`) would have its own column.
-
-```{r}
-head(data_DWP)
-```
-
-### Carcass Observations (`CO`)
-Information about each (non-trial) carcass observed during searches is stored in
-`CO_data` which is a data frame with at least 4 columns: carcass ID, the turbine
-(or unit) at which it was found, the date it was found and its distance from the
-turbine center. In `data_CO` we also have turbine type, species, and species
-group variables by which we will later summarize mortality estimates.
-```{r}
-head(data_CO)
-```
-
-### Estimating Searcher Efficiency and Carcass Persistence Parameters
-Searcher efficiency and carcass persistence parameters are estimated by fitting
-models using functions `pkm` and `cpm` (carcass persistence model) which are
-patterned after familar R functions such as `lm` and `glm`. The "pk" in `pkm`
-refers to GenEst's model of searcher efficiency which includes two parameters:
-`p`, which is the initial searcher efficiency for carcasses on the first search
-after they have arrived, and `k`, which is a parameter governing the decrease
-in searcher efficiency in later searches. In this relatively simple example,
-our SE and CP field trials were conducted for one carcass size (bat) on one
-type of terrain (roads and pads) in three seasons (spring, summer, fall). The
-only potential predictor variable we have is `Season`, which is entered as a
-column in both `data_SE` and `data_CP`.
-
-Searcher efficiency is the probability of detection of a carcass that is present
-in the searched area at the time of search. Searcher efficiency typically
-decreases with carcass age because older carcasses tend to become harder to find
-as they accumulate dust or debris, fall deeper into vegetation, get blown
-against objects or into holes, decay, or get partially scavenged. In addition,
-carcasses missed in one search tend to be more likely to be missed in subsequent
-searches because the relatively easy-to-find carcasses are preferentially
-removed in the first searches after carcass arrival, leaving mostly the
-harder-to-find carcasses available in subsequences searches. GenEst accounts
-for a non-constant searcher efficiency using two parameters, `p` (searcher
-efficiency on the first search after carcass arrivals) and `k` (proportional
-change in searcher efficiency with each successive search). The `k` parameter
-can be estimated from field trials if carcasses that are not discovered in the
-first search after arrival are left in the field for possible discovery in
-later searches.
-
-`p` and `k` may both depend on covariates such as season, visibility class, or
-carcass size, and GenEst allows for them to be modeled as functions of different
-covariate combinations.
-
-```{r}
-model_SE <- pkm(p ~ Season, k ~ 1, data = data_SE)
-class(model_SE)
-model_SE
-```
-
-NOTE: The `pkm` family of functions by default interprets columns with names
-that begin with and "s" or "S" and end with a number contain search results data
-(carcass found = 0, not found = 1). A user can override the auto-parsing by
-explicitly listing the names of the search data columns in a vector of character
-strings in the `obsCol` argument.
-
-The probability of a carcass persisting a given length of time without being
-removed by scavengers (or other factors) is modeled as a Weibull, lognormal,
-loglogistic, or exponential distribution. Like the `p` and `k` parameters for
-searcher efficiency, the location and scale parameters (Therneau 2015) of the
-persistence distribution may depend on covariates. GenEst allows for them to be
-modeled as separate functions of predictor combinations.
-
-```{r}
-model_CP <- cpm(l ~ Season, s ~ Season, data = data_CP, dist = "weibull",
- left = "LastPresent", right = "FirstAbsent")
-class(model_CP)
-model_CP
-```
-The model summary shows descriptive statistics for the cellwise estimates of
-the `l` and `s` parameters. The `location` and `scale` parameterization is
-common in survival analysis, but the `pda` and `pdb` parameterization (Dalthorp
-and Huso, 2014) is also shown. These parameterizations are convenient to work
-with in statistical calculations but are not as convenient for giving users
-quick insight into the distributions, so a third set of summary statistics
-about the fitted distributions is also given. Namely, the median persistence
-time and the `r` statistic, which is the probability that a carcass persists
-until the first search after arrival (assuming uniformly distributed arrival
-times within the interval). Clearly, `r` depends on the length of the search
-interval, and the table shows `r` for intervals of 1, 3, 7, 14, and 28 days. A
-rough, back-of-the-envelope calculation for the probability of observing a
-carcass that arrives at a site during the monitored period would be
-`DWP * r * p * f`, which is `DWP` = fraction of carcasses that arrive in the
-area searched at a unit, `r` = fraction of carcasses that persist until a
-search, `p` = fraction of carcasses found on the first search after arrival
-(given that they persisted), and `f` = fraction of carcasses that arrive at
-the units searched.
-
-In other scenarios we might consider other predictors, like the visibility of
-the ground searched or the search team. We might also be interested in carcasses
-of different sizes (e.g., large, medium, and small birds instead of or in
-addition to bats). We are not restricted to using the same predictors of both SE
-and CP. The modeling complexity increases with each additional predictor, but,
-in theory, any number of predictors can be used. The only rule is that
-sufficient numbers of trial carcasses must be placed in each cell combination
-of factor levels among the selected predictors. For example, if we were to place
-15 carcasses for each cell for predictors that include season (spring, summer,
-fall, winter), size (S, M, L, B), visibility (RP, M, D), search team (dogs,
-humans), and turbine type (small, medium, large), we'd need
-15 x 4 x 4 x 3 x 2 x 2 = 2880 carcasses. Typically, the number of predictors is
-limited to a few key variables.
-
-### Mortality Estimation
-Each carcass's contribution to the total mortality in each search interval is
-estimated using the `estM` function.
-
-```{r, fig.width = 7, fig.height = 5, fig.align = 'center'}
-Mhat <- estM(nsim = 1000, data_CO = data_CO, data_SS = data_SS,
- data_DWP = data_DWP, model_SE = model_SE, model_CP = model_CP,
- unitCol = "Turbine", COdate = "DateFound")
-
-summary(Mhat)
-plot(Mhat)
-```
-
-Mortality estimates may be partitioned or split into desired categories, such as
-species, season, or turbine type. Splits may be performed according to
-characteristics of the carcasses or where they were found (e.g., species,
-turbine or other variable found in `data_CO`) or when they were found (e.g.,
-season or other variable associated with search schedule and found in `data_SS`,
-or a vector of specific times).
-
-Mortality by `Species` (a CO split because it is a column in the CO file):
-```{r, fig.width = 5, fig.height = 5, fig.align = 'center'}
-M_species <- calcSplits(M = Mhat, split_CO = "Species", data_CO = data_CO)
-summary(M_species)
-plot(M_species)
-```
-
-Mortality estimates may also be split by temporal variables that are represented
-as columns in `data_SS` or as numeric vectors spanning the monitoring
-season (from day 0 to length of monitoring season). If several temporal splits
-are to be calculated, creating a specially formatted `prepSS` object for the
-search schedule can streamline the calculations.
-```{r}
-SSdat <- prepSS(data_SS)
-```
-
-Mortality by `Season` (an SS split because it is a column in the SS file):
-```{r Season Split, fig.width = 6.5, fig.height = 5.25, fig.align = 'center'}
-M_season <- calcSplits(M = Mhat, split_SS = "Season", data_SS = SSdat,
- split_CO = NULL, data_CO = data_CO)
-summary(M_season)
-plot(M_season)
-```
-
-Mortality by month (a temporal split that spans the monitoring period):
-```{r Temporal Split, fig.width = 7, fig.height = 5, fig.align = 'center'}
-M_month <- calcSplits(M = Mhat, split_time = seq(0, max(SSdat$days), by = 28),
- data_SS = SSdat, data_CO = data_CO)
-summary(M_month)
-plot(M_month)
-```
-Temporal splits that divide the monitoring season into separate time intervals
-(like season or month) can be plotted as the number per interval
-(`rate = FALSE`, which is the default arg in `calcSplits`) or the number per
-unit time (`rate = TRUE`).
-```{r Time unit Split, fig.width = 7, fig.height = 5, fig.align = 'center'}
-M_various_times <- calcSplits(M = Mhat,
- split_time = c(seq(0, 90, by = 15), 120, 150, seq(155, 200, by = 5)),
- data_SS = SSdat, data_CO = data_CO)
-plot(M_various_times)
-plot(M_various_times, rate = TRUE)
-```
-
-Finally, splits can be calculated for combinations of splitting covariates,
-like species by season or species group by turbine type. No more than two
-splitting covariates may be used in one call to `calcSplits` and at most one
-temporal split may be used (whether it is an SS split or a vector of times).
-
-```{r Species and Season, fig.width = 4, fig.height = 6, fig.align = 'center'}
-M_species_by_season <- calcSplits(M = Mhat,
- split_CO = "Species", data_CO = data_CO,
- split_SS = "Season", data_SS = SSdat)
-plot(M_species_by_season)
-```
-
-## Example 2: Estimating Bird and Bat Mortality from Searches on Varied Ground
-Thorough searches out to a radius of 60 m from each turbine were conducted at 23
-out of 100 turbines. The searched area was divided into three visibility classes
-(`RP`, `M`, `D`) according the difficulty of finding carcasses.
-
-Searcher efficiency and carcass persistence would be expected to vary with
-carcass size (sparrow, eagle, bat), ground characteristics (road & pad,
-cleared field, vegetation type), season, etc. In this example, we perform a full
-analysis of scenario with four classes of carcass (`lrg`, `med`, `sml`, and
-`bat`), three visibility classes (difficult = `D`, moderate = `M`, and road &
-pad = `RP`), and three seasons (`spring`, `summer`, and `fall`).
-
-The required data is stored in `wind_cleared`, a list of data frames with results
-for searcher efficiency (`SE`) and carcass persistence trials (`CP`), search
-schedules for all turbines (`SS`), the search coverage or density weighted
-proportion (`DWP`) of area searched at each turbine (i.e., the fraction of
-carcasses expected to fall in the search plots), and carcass observation (`CO`)
-data.
-
-Load the full data set into R:
-```{r}
-data(wind_cleared)
-names(wind_cleared)
-```
-To streamline the notation, extract the data from the `wind_cleared` list into
-its components:
-```{r}
-data_SE <- wind_cleared$SE
-data_CP <- wind_cleared$CP
-data_SS <- wind_cleared$SS
-data_DWP <- wind_cleared$DWP
-data_CO <- wind_cleared$CO
-```
-### Searcher Efficiency and Carcass Persistence Trials
-In searcher efficiency and carcass persistence trials, 15 trial
-carcasses were placed in each combination of visibility class (`D`, `M`, `RP`),
-season (`spring`, `summer`, `fall`), and size class (`lrg`, `med`, `sml`, `bat`).
-Data formats are like those of example 1:
-```{r}
-head(data_SE)
-head(data_CP)
-```
-
-### Searcher Efficiency Modeling
-With 36 combinations of covariate levels (3 visibilities x 3 seasons x 4 sizes)
-and two parameters (`p` and `k`), the number of possible models to consider for
-searcher efficiency is unwieldy using simple calls to `pkm`, but `pkm` has
-powerful model building and model selection capabilities that can be accessed
-via the arg list: `allCombos` and `sizeCol`, which are discussed below.
-
-When `allCombos = TRUE`, `pkm` fits the set of submodels of the given covariate
-combinations, including the full model, the null model, and
-everything in between. For example, if the parameter models are
-`p ~ Visibility * Season` and `k ~ Visibility`, `pkm` with `allCombos = TRUE`
-would fit all combinations of possible `p` models (`p ~ Visibility * Season`,
-`p ~ Visibility + Season`, `p ~ Visibility`, `p ~ Season`, and `p ~ 1`) and
-possible `k` models (`k ~ Visibility` and `k ~ 1`), or 10 models in all.
-
-Carcasses in different size classes would be expected to have different searcher
-efficiency and carcass persistence parameters and would even be likely to be
-affected by covariates in different ways. When analyzing data with carcasses in
-different size classes, it is recommended that separate CP and SE models be fit
-for size classes separately. This can be accomplished using the `sizeCol`
-argument in `pkm` and `cpm`, where `sizeCol` gives the name of the column that
-gives the carcass size classes in `data_SE`.
-
-```{r}
-pkModels <- pkm(p ~ Visibility * Season, k ~ Visibility * Season, data = data_SE,
- allCombos = TRUE, sizeCol = "Size")
-class(pkModels)
-names(pkModels)
-```
-When `allCombos = TRUE` and `sizeCol` is defined, `pkm` returns a list of sets
-of models for each size class. The sets of models for each size class include
-the full spectrum of models that can be constructed using simple combinations
-of the covariates.
-```{r}
-names(pkModels[["sml"]])
-class(pkModels[["sml"]])
-```
-
-To estimate mortality, one model for each size class must be selected from the
-long list of models fit. GenEst provides several tools for guiding the selection.
-First, the models can be listed by AICc, which gives a score for the quality of
-the model for the given data. Complicated models that use many parameters may
-fit the data more closely than a simpler model but are penalized because of
-their complexity and relative instability. The scores have meaning only in
-comparison with other models'. AICc provides a rough but useful guide for model
-selection, but should in no way be relied upon as definitive. Its utility is in
-identifying relatively poor models and in narrowing the choice of plausible
-models to a manageable number.
-
-The `aicc` function lists the fitted models in order of ${\small \Delta}$AICc
-for each size class (if applicable). For this discussion, we will focus on
-`sml` only, but for a full analysis, all size classes would need to be similarly
-analyzed.
-
-```{r}
-aicc(pkModels[["sml"]])
-```
-Preference should normally be given to models with ${\small \Delta}$AICc less
-than 6 or 7. Models with differences of less than 3 or 4 are generally
-considered indistinguishable by this measure. Choices among such models should
-be based on other criteria.
-
-Diagnostic plots can be used to identify potential problems with model fits and
-to help distinguish between models with similar AICc scores. The `plot` function
-is defined for `pkm` (one model) and `pkmSet` (set of models for a given size
-class) objects. For example, `plot(pkModels[["sml"]][[1]])` would produce the
-single figure for the first model for the `sml` size class.
-`plot(pkModels[["sml"]])` would create plots for each model fit for the `sml`
-size class. To plot a specific single model from the full set,
-use the `specificModel` argument. For example, diagnostic plots for the model
-with the lowest AICc score are shown below:
-
-```{r, fig.show = "hold", fig.width = 7, fig.height = 7}
-plot(pkModels[["sml"]], specificModel = "p ~ Visibility; k ~ 1")
-```
-
-The top row shows box plots of estimated `p` and `k` parameters for all cells
-(i.e., combinations of covariate levels, like `D.fall` for difficult visibility
-in the fall) for both the selected model (black) and the full model (gray). With
-the full model, the fits for each cell are based solely on data from that
-specific cell. The advantage is that each cell's estimates are untainted by data
-from other cells. The disadvantage is that the sample size for each estimate
-is relatively small and the error bars large. In the reduced models, estimates
-for one cell borrow strength from estimates in related cells. This gives smaller
-error bars but can lead to errors if the model structure does not properly
-reflect the dependence of searcher efficiency parameters on cell
-characteristics.
-
-In the figure, the estimates of `p` from the selected model are markedly less
-variable than the estimates from the full model, while the locations of the
-boxes are very similar for the two models. Thus, this selected model (the one
-with the lowest AICc) appears to be an improvement over the full cell model for
-estimating `p`.
-
-The ${\small \Delta}$AICc for the full model is 12.52, which indicates a serious
-deficiency in comparison to the model with the best fit,
-`"p ~ Visibility; k ~ 1"`. The boxplots for `k` highlight one particular problem
-with the fit of the reference model. In some of the cells, the boxes extend from
-0 to 1, which suggests that the reference model is unable to estimate `k` for
-the given cell. Selecting a simpler model for `k` often remedies this problem.
-If all models display this 0-1 phenomenon, a fixed `k` of 1 is appropriate if
-a smaller proportion of carcasses was found on the first search occasion than on
-later searches.
-
-By comparison, the model with the highest ${\small \Delta}$AICc (=
-`{r max(aicc(pkModels[["sml"]]))}` routinely estimates `p` and `k` either well
-above or well below the reference model but has fairly tight error bars--bad
-estimates but quite confident about them!
-
-```{r, fig.show = "hold", fig.width = 7, fig.height = 7}
-plot(pkModels[["sml"]], specificModel = "p ~ Season; k ~ Visibility * Season")
-```
-
-A similar model selection exercise gives the same form of model
-(`p ~ Visibility; k ~ 1`) for large birds, medium birds, and bats. These can all
-be collated into a list for later analysis of detection probabilities and
-mortality rates.
-
-```{r}
-pkMods <- list(
- sml = pkModels[["sml"]][["p ~ Visibility; k ~ 1"]],
- med = pkModels[["med"]][["p ~ Visibility; k ~ 1"]],
- lrg = pkModels[["lrg"]][["p ~ Visibility; k ~ 1"]],
- bat = pkModels[["bat"]][["p ~ Visibility; k ~ 1"]]
-)
-```
-
-### Carcass Persistence Modeling
-The work flow for carcass persistence modeling is similar to that for searcher
-efficiency except that in addition to selecting covariates for two different
-parameters (location = `l` and scale = `s`), there are four model forms to
-choose from: Weibull, lognormal, loglogistic, and exponential.
-
-```{r}
-cpModels <- cpm(
- l ~ Visibility * Season, s ~ Visibility * Season,
- data = data_CP, left = "LastPresent", right = "FirstAbsent",
- dist = c( "weibull", "lognormal", "loglogistic", "exponential"),
- allCombos = TRUE, sizeCol = "Size"
-)
-```
-The list of models is long:
-```{r}
-lapply(aicc(cpModels), nrow)
-aicc(cpModels[["sml"]])
-```
-It is not uncommon to see the fits for the exponential distribution at the
-bottom of the AIC list. The exponential distribution has only one parameter and
-does not have nearly as much flexibility as the others, which each have
-two-parameters. An implicit assumption of the exponential model is that the
-scavenging rate is constant, regardless of carcass age. When that assumption is
-not met, the exponential provides an inferior fit.
-
-To compare among a set of cp models with plausible AICs, use, for example,
-`plot(cpModels[["sml"]])` to browse through all the models or
-`plot(cpModels[["sml"]][["p ~ Visibility; k ~ 1"]])` to view a single model
-specified by name. It can be seen from the AICc table
-(`aicc(cpModels[["sml"]])`) that the top 10 models according to AICc are:
-
-```{r}
-cp_smlCandidates <-
- names(cpModels[["sml"]])[c(25, 24, 15, 19, 20, 10, 14, 22, 74, 21)]
-cp_smlCandidates
-```
-
-These can be compared in graphs as follows:
-
-```{r, eval = F}
-plot(cpModels[["sml"]], specificModel = cp_smlCandidates)
-```
-
-The figure shows the raw persistence data (fraction of carcasses remaining after
-the given time) for each cell, as a black stair case with Kaplan-Meier
-confidence intervals as dashed lines. In addition, the fitted curves for each of
-the distributions are shown in color, with the `specificModel` distribution
-having a thicker than the others. The two-parameter models (Weibull, lognormal,
-and loglogistic) tend to be very similar and a relatively close fit to the data.
-The exponential model tends to be somewhat removed from the others. Clicking on
-the graphing window brings up the next set of figures.
-
-We're looking for a good fit between the selected model and the data in as many
-cells as possible. The first several models among the `cp_smlCandidates` seems
-to provide a reasonably good fit in all cells, although there seems to be a
-trade-off between fitting the RP.spring cell well or fitting the RP.summer cell
-well. `Visibility` occurs frequently in the top models, while `Season` appears
-more frequently in the bottom models. This indicates that `Season` is probably
-not a strong predictor of carcass persistence, while `Visibility` is.
-
-Selecting the top AICc model for use in mortality estimation:
-```{r}
-cp_sml <- cpModels[["sml"]][[cp_smlCandidates[1]]]
-```
-
-Following a similar model selection process for the other size classes, we
-select the following:
-
-```{r}
-cp_med <- cpModels[["med"]][["dist: weibull; l ~ Visibility; s ~ Season"]]
-cp_lrg <- cpModels[["lrg"]][["dist: exponential; l ~ Visibility + Season; NULL"]]
-cp_bat <- cpModels[["bat"]][["dist: weibull; l ~ Visibility + Season; s ~ 1"]]
-```
-
-NOTE: For large carcasses, the exponential distribution was at the top of the AICc
-list. The exponential requires only one parameter (`l`), so no scale parameter
-is provided in the model.
-
-Again, we collate the models into a list for later analysis of detection
-probabilities and mortality rates.
-
-```{r}
-cpMods <- list(
- sml = cp_sml,
- med = cp_med,
- lrg = cp_lrg,
- bat = cp_bat
-)
-```
-
-### Mortality Estimation
-Each carcass's contribution to the total mortality in each search interval is
-estimated using the `estM` function. The function call is largely similar that
-used in the simple scenario discussed in example 1. However, there are some
-important differences. First, `model_SE` and `model_CP` are lists of models, one
-element for each size class. In addition, the name of the size class variable is
-provided as `sizeCol = "Size"`. Finally, the `frac` argument represents the
-sampling fraction or the fraction of carcasses expected to fall at the units
-that were searched. In this example, 23 out of 100 turbines were searched, and
-`frac` is set equal to 0.23 under the assumption that the mortality rates at the
-unsearched turbines did not differ substantially from the rates at the searched
-turbines.
-
-```{r Mhat plot, fig.height = 4, fig.width = 7, fig.align = 'center'}
-Mhat <- estM(nsim = 1000, data_CO = data_CO, data_SS = data_SS, frac = 0.23,
- data_DWP = data_DWP, model_SE = pkMods, model_CP = cpMods,
- sizeCol = "Size", unitCol = "Turbine", COdate = "DateFound")
-
-summary(Mhat)
-plot(Mhat)
-```
-
-This estimate is for the total number of fatalities among all size classes
-combined, from hummingbirds and bats to eagles and may be too vague to be very
-useful. Fortunately, mortality estimates
-may be partitioned or split into desired categories, such as species, size, or
-season. Splits may be performed according to characteristics
-of the carcasses or where they were found (e.g., species, turbine or other
-variable found in `data_CO`) or when they were found (e.g., season or other
-variable associated with search schedule and found in ``data_SS``, or a vector of
-specific times).
-
-Carcasses were categorized not only by size but also by species, species group,
-the type of turbine they were found at, the visibility class of the ground
-where they were found, and distance from nearest turbine.
-
-Although species groups may extend across different size classes (e.g.,
-`raptors` could include kestrels, red-tailed hawks, and golden eagles;
-`passerines` could include sparrows and ravens), splits according to species
-group can easily be accomplished:
-
-```{r Species Group Plot, fig.height = 5, fig.width = 5, fig.align = 'center'}
-M_speciesGroup <- calcSplits(M = Mhat,
- split_CO = "SpeciesGroup", data_CO = data_CO)
-summary(M_speciesGroup)
-plot(M_speciesGroup)
-```
-
-Split by species and season:
-```{r Split Species and Season, fig.height = 12, fig.width = 4, fig.align = 'center'}
-M_speciesseason <- calcSplits(M = Mhat,
- split_CO = "Species", data_CO = data_CO, split_SS = "Season", data_SS = data_SS)
-summary(M_speciesseason)
-plot(M_speciesseason)
-```
-
-There are so many vertical panels that it is difficult to glean any useful
-information out of the graph. However, the panels may be transposed and graphed
-for better interpretability:
-
-```{r Transposed Species Season, fig.height = 7, fig.width = 7, fig.align = 'center'}
-plot(transposeSplits(M_speciesseason))
-```
\ No newline at end of file
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-
-<h1 class="title toc-ignore">GenEst - A Tutorial with Wind Examples</h1>
-<h4 class="author"><em>Daniel Dalthorp</em></h4>
-<h4 class="date"><em>2018-12-05</em></h4>
-
-
-
-<p>```</p>
-<div id="introduction-tutorial-with-examples" class="section level2">
-<h2>Introduction: Tutorial with Examples</h2>
-<p>This tutorial provides an introduction to the array of command line tools <strong>GenEst</strong> provides for estimating carcass arrivals and detection probabilities. The approach is to walk through analyses of realistic but fictitious data sets representing studies of bird and bat mortality at a wind power facility.</p>
-<p>The general steps in the analysis are:</p>
-<ol style="list-style-type: decimal">
-<li>Construct a model for Searcher Efficiency</li>
-<li>Construct a model for Carcass Persistance</li>
-<li>Estimate mortality</li>
-<li>Specify the type of summary desired (e.g., mortality by species and season)</li>
-</ol>
-<p>Data required for a full analysis include results of searcher efficiency trials, results of carcass persistence trials, search schedules for all units searched, the search coverage within each unit, and results of periodic carcass surveys. More information about the kinds of data required can be found in the User Guide. For convenience, data required in this tutorial are available in R as packaged lists, which can readily be loaded as described below. Alternatively, the data may be downloaded into zipped .csv files from the GUI.</p>
-<p>To perform the analyses illustrated in the tutorial, begin by starting R and loading GenEst from the command line: <code>library(GenEst)</code></p>
-</div>
-<div id="example-1-estimating-bat-mortality-from-searches-on-roads-and-pads" class="section level2">
-<h2>Example 1: Estimating Bat Mortality from Searches on Roads and Pads</h2>
-<p>Searcher efficiency and carcass persistence would be expected to vary with carcass size (sparrow, eagle, bat), ground characteristics (road & pad, cleared field, vegetation type), season, etc. In this first example, we limit the analysis to one carcass size (<code>bat</code>) and one ground visibility class (<code>RP</code> = road and pad). A more complicated scenario is analyzed in example 2.</p>
-<p>The required data is stored in <code>wind_RPbat</code>, a list of data frames with results for searcher efficiency (<code>SE</code>) and carcass persistence trials (<code>CP</code>), search schedules for all turbines (<code>SS</code>), the search coverage or density weighted proportion (<code>DWP</code>) of area searched at each turbine (i.e., the fraction of carcasses expected to fall in the search plots), and carcass observation (<code>CO</code>) data.</p>
-<p>Load the full data set into R:</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw">data</span>(wind_RPbat)</a>
-<a class="sourceLine" id="cb1-2" data-line-number="2"><span class="kw">names</span>(wind_RPbat)</a>
-<a class="sourceLine" id="cb1-3" data-line-number="3"><span class="co">#> [1] "SE" "CP" "SS" "DWP" "CO"</span></a></code></pre></div>
-<p>To streamline the notation, extract the data from the <code>wind_RPbat</code> list into its components:</p>
-<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1">data_SE <-<span class="st"> </span>wind_RPbat<span class="op">$</span>SE</a>
-<a class="sourceLine" id="cb2-2" data-line-number="2">data_CP <-<span class="st"> </span>wind_RPbat<span class="op">$</span>CP</a>
-<a class="sourceLine" id="cb2-3" data-line-number="3">data_SS <-<span class="st"> </span>wind_RPbat<span class="op">$</span>SS</a>
-<a class="sourceLine" id="cb2-4" data-line-number="4">data_DWP <-<span class="st"> </span>wind_RPbat<span class="op">$</span>DWP</a>
-<a class="sourceLine" id="cb2-5" data-line-number="5">data_CO <-<span class="st"> </span>wind_RPbat<span class="op">$</span>CO</a></code></pre></div>
-<div id="searcher-efficiency-se" class="section level3">
-<h3>Searcher Efficiency (<code>SE</code>)</h3>
-<p>Searcher efficiency trials were conducted on roads and pads, with a total of 60 fresh carcasses placed in the field over the course of the entire monitoring period, evenly divided among seasons (spring, summer, fall). Carcasses that were later discovered by search teams during the course of normal carcass surveys were removed from the field. Carcasses were left in the field for up to 5 searches after carcass placement.</p>
-<p>Results of the SE field trials are stored in the <code>data_SE</code> data frame:</p>
-<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1"><span class="kw">head</span>(data_SE)</a>
-<a class="sourceLine" id="cb3-2" data-line-number="2"><span class="co">#> pkID Season s1 s2 s3 s4 s5</span></a>
-<a class="sourceLine" id="cb3-3" data-line-number="3"><span class="co">#> 1 pk1 spring 0 NA NA NA NA</span></a>
-<a class="sourceLine" id="cb3-4" data-line-number="4"><span class="co">#> 2 pk2 spring 0 0 NA NA NA</span></a>
-<a class="sourceLine" id="cb3-5" data-line-number="5"><span class="co">#> 3 pk3 spring 1 NA NA NA NA</span></a>
-<a class="sourceLine" id="cb3-6" data-line-number="6"><span class="co">#> 4 pk4 spring 1 NA NA NA NA</span></a>
-<a class="sourceLine" id="cb3-7" data-line-number="7"><span class="co">#> 5 pk5 spring 0 NA NA NA NA</span></a>
-<a class="sourceLine" id="cb3-8" data-line-number="8"><span class="co">#> 6 pk6 spring 1 NA NA NA NA</span></a></code></pre></div>
-<p>Columns <code>s1, s2, ..., s5</code> show the fate of carcass <code>pkID</code> on the 1st, 2nd, … 5th searches after the carcass was placed. A 1 indicates that the carcass was discovered, a 0 indicates that the carcass was present but not discovered, and NA indicates that the carcass was no longer present for discovery or no search was conducted.</p>
-</div>
-<div id="carcass-persistence-cp" class="section level3">
-<h3>Carcass Persistence (<code>CP</code>)</h3>
-<p>Carcass persistence trials were conducted on roads and pads, with a total of 60 fresh carcasses placed in the field over the course of the entire monitoring period, evenly divided among seasons (spring, summer, fall). Carcasses were checked approximately 1, 2, 3, 4, 7, 10, 14, 21, and 28 days after placement in the field. Exact times were entered as decimal fractions of days after placement.</p>
-<p>Results of the SE field trials are stored in the <code>data_CP</code> data frame:</p>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1"><span class="kw">head</span>(data_CP)</a>
-<a class="sourceLine" id="cb4-2" data-line-number="2"><span class="co">#> cpID Season LastPresent FirstAbsent</span></a>
-<a class="sourceLine" id="cb4-3" data-line-number="3"><span class="co">#> 1 cp1 spring 0.00 0.93</span></a>
-<a class="sourceLine" id="cb4-4" data-line-number="4"><span class="co">#> 2 cp2 spring 0.98 1.97</span></a>
-<a class="sourceLine" id="cb4-5" data-line-number="5"><span class="co">#> 3 cp3 spring 0.00 1.01</span></a>
-<a class="sourceLine" id="cb4-6" data-line-number="6"><span class="co">#> 4 cp4 spring 13.99 21.13</span></a>
-<a class="sourceLine" id="cb4-7" data-line-number="7"><span class="co">#> 5 cp5 spring 0.00 1.17</span></a>
-<a class="sourceLine" id="cb4-8" data-line-number="8"><span class="co">#> 6 cp6 spring 20.95 27.97</span></a></code></pre></div>
-<p>Exact persistence times are not known, but a carcass that was present at one check and absent at the next check is assumed to have been removed at some point in the interval. The left endpoint of the interval was entered as <code>LastPresent</code> and the right endpoint as <code>FirstAbsent</code>. For carcasses that had not been scavenged by the end of the study, <code>LastPresent</code> is the time of the last check and <code>FirstAbsent</code> is <code>Inf</code>. For carcasses whose removal time is known exactly (e.g., scavenging was recorded by camera), <code>LastPresent = FirstAbsent</code>. The <code>Season</code> column gives the season at the time the carcass was placed in the field.</p>
-</div>
-<div id="search-schedules-ss" class="section level3">
-<h3>Search Schedules (<code>SS</code>)</h3>
-<p>Carcass searches were conducted on roads and pads within a 120 m radius from all 100 turbines at a fictitious wind power facility. Monitoring began on 1955-04-15 and continued through 1955-11-01. Searches spanned 3 seasons: spring, summer, fall. Search intervals varied by turbine and by time of year, ranging from daily searches at some turbines in the fall and searches once every 12 days in the spring at some other turbines. Search schedules for all turbines are stored in <code>data_SS</code>, which is a data frame with a column for search dates (including all dates that any turbine was searched); a column of 0s and 1s for each turbine, indication whether it was searched on the given date; and zero or more optional columns giving additional information about the date (e.g., season).</p>
-<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="kw">head</span>(data_SS[, <span class="dv">1</span><span class="op">:</span><span class="dv">10</span>])</a>
-<a class="sourceLine" id="cb5-2" data-line-number="2"><span class="co">#> SearchDate Season t1 t2 t3 t4 t5 t6 t7 t8</span></a>
-<a class="sourceLine" id="cb5-3" data-line-number="3"><span class="co">#> 1 1955-04-15 spring 0 0 0 1 0 0 0 1</span></a>
-<a class="sourceLine" id="cb5-4" data-line-number="4"><span class="co">#> 2 1955-04-18 spring 1 0 0 0 1 0 0 0</span></a>
-<a class="sourceLine" id="cb5-5" data-line-number="5"><span class="co">#> 3 1955-04-21 spring 0 1 0 0 0 1 0 0</span></a>
-<a class="sourceLine" id="cb5-6" data-line-number="6"><span class="co">#> 4 1955-04-24 spring 0 0 1 0 0 0 1 0</span></a>
-<a class="sourceLine" id="cb5-7" data-line-number="7"><span class="co">#> 5 1955-04-27 spring 0 0 0 1 0 0 0 1</span></a>
-<a class="sourceLine" id="cb5-8" data-line-number="8"><span class="co">#> 6 1955-04-30 spring 1 0 0 0 1 0 0 0</span></a></code></pre></div>
-<p>Note that we have only displayed a few of the turbine columns - there are 100 turbine columns altogether (t1, …, t100).</p>
-</div>
-<div id="density-weighted-proportion-dwp" class="section level3">
-<h3>Density Weighted Proportion (<code>DWP</code>)</h3>
-<p>The density-weighted proportion (<code>DWP</code>) is the expected fraction of carcasses that fell in the searched area. Carcass density is not the same at all distances from a turbine, but typically rises over a short distance then decreases eventually to 0. Searches were conducted on roads and pads within a 120 m radius from all 100 turbines to provide sufficient data with which to model the change in density with distance and from this, accurately calculate the fraction of all carcasses that are expected to land on road and pad surrounding each turbine (density-weighted proportion or DWP). The configuration of the roads and pads differs among turbines, hence the DWP must be calculated for each turbine. DWPs for bats at each turbine are stored in <code>data_DWP</code>, which is a data frame with a column for turbine name (note that turbine names also must be included among column names in <code>data_SS</code>, which gives the search schedule at each turbine) and a column of DWP labeled <code>bat</code>. In other studies where, for example, mortality of birds might be of interest, <code>DWP</code> would be expected vary with carcass size or species (e.g., the spatial distributions of bats and large birds around a turbine would likely differ from one another). In that case, each carcass size class (<code>large</code>, <code>medium</code>, <code>small</code>, <code>bat</code>) would have its own column.</p>
-<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1"><span class="kw">head</span>(data_DWP)</a>
-<a class="sourceLine" id="cb6-2" data-line-number="2"><span class="co">#> Turbine bat</span></a>
-<a class="sourceLine" id="cb6-3" data-line-number="3"><span class="co">#> 1 t1 0.183</span></a>
-<a class="sourceLine" id="cb6-4" data-line-number="4"><span class="co">#> 2 t2 0.192</span></a>
-<a class="sourceLine" id="cb6-5" data-line-number="5"><span class="co">#> 3 t3 0.205</span></a>
-<a class="sourceLine" id="cb6-6" data-line-number="6"><span class="co">#> 4 t4 0.194</span></a>
-<a class="sourceLine" id="cb6-7" data-line-number="7"><span class="co">#> 5 t5 0.179</span></a>
-<a class="sourceLine" id="cb6-8" data-line-number="8"><span class="co">#> 6 t6 0.171</span></a></code></pre></div>
-</div>
-<div id="carcass-observations-co" class="section level3">
-<h3>Carcass Observations (<code>CO</code>)</h3>
-<p>Information about each (non-trial) carcass observed during searches is stored in <code>CO_data</code> which is a data frame with at least 4 columns: carcass ID, the turbine (or unit) at which it was found, the date it was found and its distance from the turbine center. In <code>data_CO</code> we also have turbine type, species, and species group variables by which we will later summarize mortality estimates.</p>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1"><span class="kw">head</span>(data_CO)</a>
-<a class="sourceLine" id="cb7-2" data-line-number="2"><span class="co">#> carcID Turbine TurbineType DateFound Species SpeciesGroup Distance</span></a>
-<a class="sourceLine" id="cb7-3" data-line-number="3"><span class="co">#> 1 x79 t86 Z 1955-05-03 BA bat1 7.1</span></a>
-<a class="sourceLine" id="cb7-4" data-line-number="4"><span class="co">#> 2 x37 t19 X 1955-05-06 BB bat1 5.3</span></a>
-<a class="sourceLine" id="cb7-5" data-line-number="5"><span class="co">#> 3 x175 t37 Y 1955-05-12 BB bat1 9.8</span></a>
-<a class="sourceLine" id="cb7-6" data-line-number="6"><span class="co">#> 4 x181 t91 Z 1955-05-12 BA bat1 2.7</span></a>
-<a class="sourceLine" id="cb7-7" data-line-number="7"><span class="co">#> 5 x203 t38 Y 1955-05-15 BA bat1 3.8</span></a>
-<a class="sourceLine" id="cb7-8" data-line-number="8"><span class="co">#> 6 x291 t7 X 1955-05-18 BB bat1 112.3</span></a></code></pre></div>
-</div>
-<div id="estimating-searcher-efficiency-and-carcass-persistence-parameters" class="section level3">
-<h3>Estimating Searcher Efficiency and Carcass Persistence Parameters</h3>
-<p>Searcher efficiency and carcass persistence parameters are estimated by fitting models using functions <code>pkm</code> and <code>cpm</code> (carcass persistence model) which are patterned after familar R functions such as <code>lm</code> and <code>glm</code>. The “pk†in <code>pkm</code> refers to GenEst’s model of searcher efficiency which includes two parameters: <code>p</code>, which is the initial searcher efficiency for carcasses on the first search after they have arrived, and <code>k</code>, which is a parameter governing the decrease in searcher efficiency in later searches. In this relatively simple example, our SE and CP field trials were conducted for one carcass size (bat) on one type of terrain (roads and pads) in three seasons (spring, summer, fall). The only potential predictor variable we have is <code>Season</code>, which is entered as a column in both <code>data_SE</code> and <code>data_CP</code>.</p>
-<p>Searcher efficiency is the probability of detection of a carcass that is present in the searched area at the time of search. Searcher efficiency typically decreases with carcass age because older carcasses tend to become harder to find as they accumulate dust or debris, fall deeper into vegetation, get blown against objects or into holes, decay, or get partially scavenged. In addition, carcasses missed in one search tend to be more likely to be missed in subsequent searches because the relatively easy-to-find carcasses are preferentially removed in the first searches after carcass arrival, leaving mostly the harder-to-find carcasses available in subsequences searches. GenEst accounts for a non-constant searcher efficiency using two parameters, <code>p</code> (searcher efficiency on the first search after carcass arrivals) and <code>k</code> (proportional change in searcher efficiency with each successive search). The <code>k</code> parameter can be estimated from field trials if carcasses that are not discovered in the first search after arrival are left in the field for possible discovery in later searches.</p>
-<p><code>p</code> and <code>k</code> may both depend on covariates such as season, visibility class, or carcass size, and GenEst allows for them to be modeled as functions of different covariate combinations.</p>
-<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" data-line-number="1">model_SE <-<span class="st"> </span><span class="kw">pkm</span>(p <span class="op">~</span><span class="st"> </span>Season, k <span class="op">~</span><span class="st"> </span><span class="dv">1</span>, <span class="dt">data =</span> data_SE)</a>
-<a class="sourceLine" id="cb8-2" data-line-number="2"><span class="kw">class</span>(model_SE)</a>
-<a class="sourceLine" id="cb8-3" data-line-number="3"><span class="co">#> [1] "pkm" "list"</span></a>
-<a class="sourceLine" id="cb8-4" data-line-number="4">model_SE</a>
-<a class="sourceLine" id="cb8-5" data-line-number="5"><span class="co">#> $call</span></a>
-<a class="sourceLine" id="cb8-6" data-line-number="6"><span class="co">#> pkm0(formula_p = formula_p, formula_k = formula_k, data = data, </span></a>
-<a class="sourceLine" id="cb8-7" data-line-number="7"><span class="co">#> obsCol = obsCol, kFixed = kFixed, kInit = kInit, CL = CL, </span></a>
-<a class="sourceLine" id="cb8-8" data-line-number="8"><span class="co">#> quiet = quiet)</span></a>
-<a class="sourceLine" id="cb8-9" data-line-number="9"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb8-10" data-line-number="10"><span class="co">#> $formula_p</span></a>
-<a class="sourceLine" id="cb8-11" data-line-number="11"><span class="co">#> p ~ Season</span></a>
-<a class="sourceLine" id="cb8-12" data-line-number="12"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb8-13" data-line-number="13"><span class="co">#> $formula_k</span></a>
-<a class="sourceLine" id="cb8-14" data-line-number="14"><span class="co">#> k ~ 1</span></a>
-<a class="sourceLine" id="cb8-15" data-line-number="15"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb8-16" data-line-number="16"><span class="co">#> $predictors</span></a>
-<a class="sourceLine" id="cb8-17" data-line-number="17"><span class="co">#> [1] "Season"</span></a>
-<a class="sourceLine" id="cb8-18" data-line-number="18"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb8-19" data-line-number="19"><span class="co">#> $AICc</span></a>
-<a class="sourceLine" id="cb8-20" data-line-number="20"><span class="co">#> [1] 104.42</span></a>
-<a class="sourceLine" id="cb8-21" data-line-number="21"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb8-22" data-line-number="22"><span class="co">#> $convergence</span></a>
-<a class="sourceLine" id="cb8-23" data-line-number="23"><span class="co">#> [1] 0</span></a>
-<a class="sourceLine" id="cb8-24" data-line-number="24"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb8-25" data-line-number="25"><span class="co">#> $cell_pk</span></a>
-<a class="sourceLine" id="cb8-26" data-line-number="26"><span class="co">#> cell n p_median p_lower p_upper k_median k_lower k_upper</span></a>
-<a class="sourceLine" id="cb8-27" data-line-number="27"><span class="co">#> 1 fall 20 0.618 0.431 0.776 0.921 0.177 0.998</span></a>
-<a class="sourceLine" id="cb8-28" data-line-number="28"><span class="co">#> 2 spring 20 0.440 0.280 0.613 0.921 0.177 0.998</span></a>
-<a class="sourceLine" id="cb8-29" data-line-number="29"><span class="co">#> 3 summer 20 0.619 0.437 0.773 0.921 0.177 0.998</span></a>
-<a class="sourceLine" id="cb8-30" data-line-number="30"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb8-31" data-line-number="31"><span class="co">#> $CL</span></a>
-<a class="sourceLine" id="cb8-32" data-line-number="32"><span class="co">#> [1] 0.9</span></a></code></pre></div>
-<p>NOTE: The <code>pkm</code> family of functions by default interprets columns with names that begin with and “s†or “S†and end with a number contain search results data (carcass found = 0, not found = 1). A user can override the auto-parsing by explicitly listing the names of the search data columns in a vector of character strings in the <code>obsCol</code> argument.</p>
-<p>The probability of a carcass persisting a given length of time without being removed by scavengers (or other factors) is modeled as a Weibull, lognormal, loglogistic, or exponential distribution. Like the <code>p</code> and <code>k</code> parameters for searcher efficiency, the location and scale parameters (Therneau 2015) of the persistence distribution may depend on covariates. GenEst allows for them to be modeled as separate functions of predictor combinations.</p>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" data-line-number="1">model_CP <-<span class="st"> </span><span class="kw">cpm</span>(l <span class="op">~</span><span class="st"> </span>Season, s <span class="op">~</span><span class="st"> </span>Season, <span class="dt">data =</span> data_CP, <span class="dt">dist =</span> <span class="st">"weibull"</span>,</a>
-<a class="sourceLine" id="cb9-2" data-line-number="2"> <span class="dt">left =</span> <span class="st">"LastPresent"</span>, <span class="dt">right =</span> <span class="st">"FirstAbsent"</span>)</a>
-<a class="sourceLine" id="cb9-3" data-line-number="3"><span class="kw">class</span>(model_CP)</a>
-<a class="sourceLine" id="cb9-4" data-line-number="4"><span class="co">#> [1] "cpm" "list"</span></a>
-<a class="sourceLine" id="cb9-5" data-line-number="5">model_CP</a>
-<a class="sourceLine" id="cb9-6" data-line-number="6"><span class="co">#> $call</span></a>
-<a class="sourceLine" id="cb9-7" data-line-number="7"><span class="co">#> cpm0(formula_l = formula_l, formula_s = formula_s, data = data, </span></a>
-<a class="sourceLine" id="cb9-8" data-line-number="8"><span class="co">#> left = left, right = right, dist = dist, CL = CL, quiet = quiet)</span></a>
-<a class="sourceLine" id="cb9-9" data-line-number="9"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb9-10" data-line-number="10"><span class="co">#> $formula_l</span></a>
-<a class="sourceLine" id="cb9-11" data-line-number="11"><span class="co">#> l ~ Season</span></a>
-<a class="sourceLine" id="cb9-12" data-line-number="12"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb9-13" data-line-number="13"><span class="co">#> $formula_s</span></a>
-<a class="sourceLine" id="cb9-14" data-line-number="14"><span class="co">#> s ~ Season</span></a>
-<a class="sourceLine" id="cb9-15" data-line-number="15"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb9-16" data-line-number="16"><span class="co">#> $distribution</span></a>
-<a class="sourceLine" id="cb9-17" data-line-number="17"><span class="co">#> [1] "weibull"</span></a>
-<a class="sourceLine" id="cb9-18" data-line-number="18"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb9-19" data-line-number="19"><span class="co">#> $predictors</span></a>
-<a class="sourceLine" id="cb9-20" data-line-number="20"><span class="co">#> [1] "Season"</span></a>
-<a class="sourceLine" id="cb9-21" data-line-number="21"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb9-22" data-line-number="22"><span class="co">#> $AICc</span></a>
-<a class="sourceLine" id="cb9-23" data-line-number="23"><span class="co">#> [1] 235.51</span></a>
-<a class="sourceLine" id="cb9-24" data-line-number="24"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb9-25" data-line-number="25"><span class="co">#> $convergence</span></a>
-<a class="sourceLine" id="cb9-26" data-line-number="26"><span class="co">#> [1] 0</span></a>
-<a class="sourceLine" id="cb9-27" data-line-number="27"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb9-28" data-line-number="28"><span class="co">#> $cell_ls</span></a>
-<a class="sourceLine" id="cb9-29" data-line-number="29"><span class="co">#> cell n l_median l_lower l_upper s_median s_lower s_upper</span></a>
-<a class="sourceLine" id="cb9-30" data-line-number="30"><span class="co">#> 1 fall 20 1.163 0.612 1.715 1.360 0.932 1.986</span></a>
-<a class="sourceLine" id="cb9-31" data-line-number="31"><span class="co">#> 2 spring 20 0.857 0.169 1.545 1.679 1.198 2.354</span></a>
-<a class="sourceLine" id="cb9-32" data-line-number="32"><span class="co">#> 3 summer 20 1.246 0.561 1.930 1.688 1.155 2.467</span></a>
-<a class="sourceLine" id="cb9-33" data-line-number="33"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb9-34" data-line-number="34"><span class="co">#> $cell_ab</span></a>
-<a class="sourceLine" id="cb9-35" data-line-number="35"><span class="co">#> cell n pda_median pda_lower pda_upper pdb_median pdb_lower pdb_upper</span></a>
-<a class="sourceLine" id="cb9-36" data-line-number="36"><span class="co">#> 1 fall 20 0.735 1.073 0.504 3.200 1.844 5.557</span></a>
-<a class="sourceLine" id="cb9-37" data-line-number="37"><span class="co">#> 2 spring 20 0.596 0.835 0.425 2.356 1.184 4.688</span></a>
-<a class="sourceLine" id="cb9-38" data-line-number="38"><span class="co">#> 3 summer 20 0.592 0.866 0.405 3.476 1.752 6.890</span></a>
-<a class="sourceLine" id="cb9-39" data-line-number="39"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb9-40" data-line-number="40"><span class="co">#> $CL</span></a>
-<a class="sourceLine" id="cb9-41" data-line-number="41"><span class="co">#> [1] 0.9</span></a>
-<a class="sourceLine" id="cb9-42" data-line-number="42"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb9-43" data-line-number="43"><span class="co">#> $cell_desc</span></a>
-<a class="sourceLine" id="cb9-44" data-line-number="44"><span class="co">#> cell medianCP r1 r3 r7 r14 r28</span></a>
-<a class="sourceLine" id="cb9-45" data-line-number="45"><span class="co">#> 1 fall 1.943506 0.7877998 0.5968500 0.4035271 0.2504831 0.1362684</span></a>
-<a class="sourceLine" id="cb9-46" data-line-number="46"><span class="co">#> 2 spring 1.273808 0.6946923 0.5067622 0.3406993 0.2163474 0.1221627</span></a>
-<a class="sourceLine" id="cb9-47" data-line-number="47"><span class="co">#> 3 summer 1.871560 0.7459730 0.5779176 0.4168571 0.2838980 0.1714068</span></a></code></pre></div>
-<p>The model summary shows descriptive statistics for the cellwise estimates of the <code>l</code> and <code>s</code> parameters. The <code>location</code> and <code>scale</code> parameterization is common in survival analysis, but the <code>pda</code> and <code>pdb</code> parameterization (Dalthorp and Huso, 2014) is also shown. These parameterizations are convenient to work with in statistical calculations but are not as convenient for giving users quick insight into the distributions, so a third set of summary statistics about the fitted distributions is also given. Namely, the median persistence time and the <code>r</code> statistic, which is the probability that a carcass persists until the first search after arrival (assuming uniformly distributed arrival times within the interval). Clearly, <code>r</code> depends on the length of the search interval, and the table shows <code>r</code> for intervals of 1, 3, 7, 14, and 28 days. A rough, back-of-the-envelope calculation for the probability of observing a carcass that arrives at a site during the monitored period would be <code>DWP * r * p * f</code>, which is <code>DWP</code> = fraction of carcasses that arrive in the area searched at a unit, <code>r</code> = fraction of carcasses that persist until a search, <code>p</code> = fraction of carcasses found on the first search after arrival (given that they persisted), and <code>f</code> = fraction of carcasses that arrive at the units searched.</p>
-<p>In other scenarios we might consider other predictors, like the visibility of the ground searched or the search team. We might also be interested in carcasses of different sizes (e.g., large, medium, and small birds instead of or in addition to bats). We are not restricted to using the same predictors of both SE and CP. The modeling complexity increases with each additional predictor, but, in theory, any number of predictors can be used. The only rule is that sufficient numbers of trial carcasses must be placed in each cell combination of factor levels among the selected predictors. For example, if we were to place 15 carcasses for each cell for predictors that include season (spring, summer, fall, winter), size (S, M, L, B), visibility (RP, M, D), search team (dogs, humans), and turbine type (small, medium, large), we’d need 15 x 4 x 4 x 3 x 2 x 2 = 2880 carcasses. Typically, the number of predictors is limited to a few key variables.</p>
-</div>
-<div id="mortality-estimation" class="section level3">
-<h3>Mortality Estimation</h3>
-<p>Each carcass’s contribution to the total mortality in each search interval is estimated using the <code>estM</code> function.</p>
-<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" data-line-number="1">Mhat <-<span class="st"> </span><span class="kw">estM</span>(<span class="dt">nsim =</span> <span class="dv">1000</span>, <span class="dt">data_CO =</span> data_CO, <span class="dt">data_SS =</span> data_SS,</a>
-<a class="sourceLine" id="cb10-2" data-line-number="2"> <span class="dt">data_DWP =</span> data_DWP, <span class="dt">model_SE =</span> model_SE, <span class="dt">model_CP =</span> model_CP,</a>
-<a class="sourceLine" id="cb10-3" data-line-number="3"> <span class="dt">unitCol =</span> <span class="st">"Turbine"</span>, <span class="dt">COdate =</span> <span class="st">"DateFound"</span>)</a>
-<a class="sourceLine" id="cb10-4" data-line-number="4"></a>
-<a class="sourceLine" id="cb10-5" data-line-number="5"><span class="kw">summary</span>(Mhat)</a>
-<a class="sourceLine" id="cb10-6" data-line-number="6"><span class="co">#> median 5% 95% </span></a>
-<a class="sourceLine" id="cb10-7" data-line-number="7"><span class="co">#> 1424.16 1026.00 2144.33</span></a>
-<a class="sourceLine" id="cb10-8" data-line-number="8"><span class="kw">plot</span>(Mhat)</a></code></pre></div>
-<p><img 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" style="display: block; margin: auto;" /></p>
-<p>Mortality estimates may be partitioned or split into desired categories, such as species, season, or turbine type. Splits may be performed according to characteristics of the carcasses or where they were found (e.g., species, turbine or other variable found in <code>data_CO</code>) or when they were found (e.g., season or other variable associated with search schedule and found in <code>data_SS</code>, or a vector of specific times).</p>
-<p>Mortality by <code>Species</code> (a CO split because it is a column in the CO file):</p>
-<div class="sourceCode" id="cb11"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb11-1" data-line-number="1">M_species <-<span class="st"> </span><span class="kw">calcSplits</span>(<span class="dt">M =</span> Mhat, <span class="dt">split_CO =</span> <span class="st">"Species"</span>, <span class="dt">data_CO =</span> data_CO)</a>
-<a class="sourceLine" id="cb11-2" data-line-number="2"><span class="kw">summary</span>(M_species)</a>
-<a class="sourceLine" id="cb11-3" data-line-number="3"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb11-4" data-line-number="4"><span class="co">#> BA 42 548.10099 689.860637 811.14559 964.24152 1260.55441</span></a>
-<a class="sourceLine" id="cb11-5" data-line-number="5"><span class="co">#> BB 14 183.86290 274.716104 346.60741 447.48810 637.31207</span></a>
-<a class="sourceLine" id="cb11-6" data-line-number="6"><span class="co">#> BC 9 66.74322 115.678264 154.49280 203.57216 289.74366</span></a>
-<a class="sourceLine" id="cb11-7" data-line-number="7"><span class="co">#> BD 5 27.64681 58.094117 83.27394 119.67705 187.26175</span></a>
-<a class="sourceLine" id="cb11-8" data-line-number="8"><span class="co">#> BE 1 1.00000 4.130205 13.79527 24.95592 48.77744</span></a>
-<a class="sourceLine" id="cb11-9" data-line-number="9"><span class="co">#> attr(,"class")</span></a>
-<a class="sourceLine" id="cb11-10" data-line-number="10"><span class="co">#> [1] "splitSummary"</span></a>
-<a class="sourceLine" id="cb11-11" data-line-number="11"><span class="co">#> attr(,"CL")</span></a>
-<a class="sourceLine" id="cb11-12" data-line-number="12"><span class="co">#> [1] 0.9</span></a>
-<a class="sourceLine" id="cb11-13" data-line-number="13"><span class="co">#> attr(,"vars")</span></a>
-<a class="sourceLine" id="cb11-14" data-line-number="14"><span class="co">#> [1] "Species"</span></a>
-<a class="sourceLine" id="cb11-15" data-line-number="15"><span class="co">#> attr(,"type")</span></a>
-<a class="sourceLine" id="cb11-16" data-line-number="16"><span class="co">#> [1] "CO"</span></a>
-<a class="sourceLine" id="cb11-17" data-line-number="17"><span class="kw">plot</span>(M_species)</a></code></pre></div>
-<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
-<p>Mortality estimates may also be split by temporal variables that are represented as columns in <code>data_SS</code> or as numeric vectors spanning the monitoring season (from day 0 to length of monitoring season). If several temporal splits are to be calculated, creating a specially formatted <code>prepSS</code> object for the search schedule can streamline the calculations.</p>
-<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" data-line-number="1">SSdat <-<span class="st"> </span><span class="kw">prepSS</span>(data_SS)</a></code></pre></div>
-<p>Mortality by <code>Season</code> (an SS split because it is a column in the SS file):</p>
-<div class="sourceCode" id="cb13"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb13-1" data-line-number="1">M_season <-<span class="st"> </span><span class="kw">calcSplits</span>(<span class="dt">M =</span> Mhat, <span class="dt">split_SS =</span> <span class="st">"Season"</span>, <span class="dt">data_SS =</span> SSdat,</a>
-<a class="sourceLine" id="cb13-2" data-line-number="2"> <span class="dt">split_CO =</span> <span class="ot">NULL</span>, <span class="dt">data_CO =</span> data_CO)</a>
-<a class="sourceLine" id="cb13-3" data-line-number="3"><span class="kw">summary</span>(M_season)</a>
-<a class="sourceLine" id="cb13-4" data-line-number="4"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb13-5" data-line-number="5"><span class="co">#> spring 18.11092 303.8122 435.6746 576.6190 764.4588 1188.2073</span></a>
-<a class="sourceLine" id="cb13-6" data-line-number="6"><span class="co">#> summer 10.16308 89.2881 145.6323 191.2028 249.1176 363.6927</span></a>
-<a class="sourceLine" id="cb13-7" data-line-number="7"><span class="co">#> fall 42.72600 428.4250 525.4982 625.3096 731.4862 946.3239</span></a>
-<a class="sourceLine" id="cb13-8" data-line-number="8"><span class="co">#> attr(,"class")</span></a>
-<a class="sourceLine" id="cb13-9" data-line-number="9"><span class="co">#> [1] "splitSummary"</span></a>
-<a class="sourceLine" id="cb13-10" data-line-number="10"><span class="co">#> attr(,"CL")</span></a>
-<a class="sourceLine" id="cb13-11" data-line-number="11"><span class="co">#> [1] 0.9</span></a>
-<a class="sourceLine" id="cb13-12" data-line-number="12"><span class="co">#> attr(,"vars")</span></a>
-<a class="sourceLine" id="cb13-13" data-line-number="13"><span class="co">#> [1] "Season"</span></a>
-<a class="sourceLine" id="cb13-14" data-line-number="14"><span class="co">#> attr(,"type")</span></a>
-<a class="sourceLine" id="cb13-15" data-line-number="15"><span class="co">#> [1] "SS"</span></a>
-<a class="sourceLine" id="cb13-16" data-line-number="16"><span class="co">#> attr(,"times")</span></a>
-<a class="sourceLine" id="cb13-17" data-line-number="17"><span class="co">#> [1] 0 60 130 200</span></a>
-<a class="sourceLine" id="cb13-18" data-line-number="18"><span class="kw">plot</span>(M_season)</a></code></pre></div>
-<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
-<p>Mortality by month (a temporal split that spans the monitoring period):</p>
-<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb14-1" data-line-number="1">M_month <-<span class="st"> </span><span class="kw">calcSplits</span>(<span class="dt">M =</span> Mhat, <span class="dt">split_time =</span> <span class="kw">seq</span>(<span class="dv">0</span>, <span class="kw">max</span>(SSdat<span class="op">$</span>days), <span class="dt">by =</span> <span class="dv">28</span>),</a>
-<a class="sourceLine" id="cb14-2" data-line-number="2"> <span class="dt">data_SS =</span> SSdat, <span class="dt">data_CO =</span> data_CO)</a>
-<a class="sourceLine" id="cb14-3" data-line-number="3"><span class="kw">summary</span>(M_month)</a>
-<a class="sourceLine" id="cb14-4" data-line-number="4"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb14-5" data-line-number="5"><span class="co">#> 28 6.251583 84.088312 154.14475 225.16790 307.52325 522.4244</span></a>
-<a class="sourceLine" id="cb14-6" data-line-number="6"><span class="co">#> 56 11.733308 162.230641 259.23076 345.43886 467.18761 732.9935</span></a>
-<a class="sourceLine" id="cb14-7" data-line-number="7"><span class="co">#> 84 2.115395 2.115395 28.85184 51.70731 86.52933 163.4904</span></a>
-<a class="sourceLine" id="cb14-8" data-line-number="8"><span class="co">#> 112 4.564000 26.271999 51.91801 77.01424 106.63274 161.4349</span></a>
-<a class="sourceLine" id="cb14-9" data-line-number="9"><span class="co">#> 140 10.091810 77.671494 115.02411 147.35298 182.78795 241.2021</span></a>
-<a class="sourceLine" id="cb14-10" data-line-number="10"><span class="co">#> 168 27.783381 269.359641 350.47189 413.80292 494.00561 658.6985</span></a>
-<a class="sourceLine" id="cb14-11" data-line-number="11"><span class="co">#> 196 8.460524 52.121635 87.03031 114.07485 150.42913 223.7377</span></a>
-<a class="sourceLine" id="cb14-12" data-line-number="12"><span class="co">#> 200 0.000000 0.000000 0.00000 0.00000 0.00000 0.0000</span></a>
-<a class="sourceLine" id="cb14-13" data-line-number="13"><span class="co">#> attr(,"class")</span></a>
-<a class="sourceLine" id="cb14-14" data-line-number="14"><span class="co">#> [1] "splitSummary"</span></a>
-<a class="sourceLine" id="cb14-15" data-line-number="15"><span class="co">#> attr(,"CL")</span></a>
-<a class="sourceLine" id="cb14-16" data-line-number="16"><span class="co">#> [1] 0.9</span></a>
-<a class="sourceLine" id="cb14-17" data-line-number="17"><span class="co">#> attr(,"vars")</span></a>
-<a class="sourceLine" id="cb14-18" data-line-number="18"><span class="co">#> [1] "time"</span></a>
-<a class="sourceLine" id="cb14-19" data-line-number="19"><span class="co">#> attr(,"type")</span></a>
-<a class="sourceLine" id="cb14-20" data-line-number="20"><span class="co">#> [1] "time"</span></a>
-<a class="sourceLine" id="cb14-21" data-line-number="21"><span class="co">#> attr(,"times")</span></a>
-<a class="sourceLine" id="cb14-22" data-line-number="22"><span class="co">#> [1] 0 28 56 84 112 140 168 196 200</span></a>
-<a class="sourceLine" id="cb14-23" data-line-number="23"><span class="kw">plot</span>(M_month)</a></code></pre></div>
-<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /> Temporal splits that divide the monitoring season into separate time intervals (like season or month) can be plotted as the number per interval (<code>rate = FALSE</code>, which is the default arg in <code>calcSplits</code>) or the number per unit time (<code>rate = TRUE</code>).</p>
-<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb15-1" data-line-number="1">M_various_times <-<span class="st"> </span><span class="kw">calcSplits</span>(<span class="dt">M =</span> Mhat,</a>
-<a class="sourceLine" id="cb15-2" data-line-number="2"> <span class="dt">split_time =</span> <span class="kw">c</span>(<span class="kw">seq</span>(<span class="dv">0</span>, <span class="dv">90</span>, <span class="dt">by =</span> <span class="dv">15</span>), <span class="dv">120</span>, <span class="dv">150</span>, <span class="kw">seq</span>(<span class="dv">155</span>, <span class="dv">200</span>, <span class="dt">by =</span> <span class="dv">5</span>)),</a>
-<a class="sourceLine" id="cb15-3" data-line-number="3"> <span class="dt">data_SS =</span> SSdat, <span class="dt">data_CO =</span> data_CO)</a>
-<a class="sourceLine" id="cb15-4" data-line-number="4"><span class="kw">plot</span>(M_various_times)</a></code></pre></div>
-<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
-<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb16-1" data-line-number="1"><span class="kw">plot</span>(M_various_times, <span class="dt">rate =</span> <span class="ot">TRUE</span>)</a></code></pre></div>
-<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
-<p>Finally, splits can be calculated for combinations of splitting covariates, like species by season or species group by turbine type. No more than two splitting covariates may be used in one call to <code>calcSplits</code> and at most one temporal split may be used (whether it is an SS split or a vector of times).</p>
-<div class="sourceCode" id="cb17"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb17-1" data-line-number="1">M_species_by_season <-<span class="st"> </span><span class="kw">calcSplits</span>(<span class="dt">M =</span> Mhat,</a>
-<a class="sourceLine" id="cb17-2" data-line-number="2"> <span class="dt">split_CO =</span> <span class="st">"Species"</span>, <span class="dt">data_CO =</span> data_CO,</a>
-<a class="sourceLine" id="cb17-3" data-line-number="3"> <span class="dt">split_SS =</span> <span class="st">"Season"</span>, <span class="dt">data_SS =</span> SSdat)</a>
-<a class="sourceLine" id="cb17-4" data-line-number="4"><span class="kw">plot</span>(M_species_by_season)</a></code></pre></div>
-<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
-</div>
-</div>
-<div id="example-2-estimating-bird-and-bat-mortality-from-searches-on-varied-ground" class="section level2">
-<h2>Example 2: Estimating Bird and Bat Mortality from Searches on Varied Ground</h2>
-<p>Thorough searches out to a radius of 60 m from each turbine were conducted at 23 out of 100 turbines. The searched area was divided into three visibility classes (<code>RP</code>, <code>M</code>, <code>D</code>) according the difficulty of finding carcasses.</p>
-<p>Searcher efficiency and carcass persistence would be expected to vary with carcass size (sparrow, eagle, bat), ground characteristics (road & pad, cleared field, vegetation type), season, etc. In this example, we perform a full analysis of scenario with four classes of carcass (<code>lrg</code>, <code>med</code>, <code>sml</code>, and <code>bat</code>), three visibility classes (difficult = <code>D</code>, moderate = <code>M</code>, and road & pad = <code>RP</code>), and three seasons (<code>spring</code>, <code>summer</code>, and <code>fall</code>).</p>
-<p>The required data is stored in <code>wind_cleared</code>, a list of data frames with results for searcher efficiency (<code>SE</code>) and carcass persistence trials (<code>CP</code>), search schedules for all turbines (<code>SS</code>), the search coverage or density weighted proportion (<code>DWP</code>) of area searched at each turbine (i.e., the fraction of carcasses expected to fall in the search plots), and carcass observation (<code>CO</code>) data.</p>
-<p>Load the full data set into R:</p>
-<div class="sourceCode" id="cb18"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb18-1" data-line-number="1"><span class="kw">data</span>(wind_cleared)</a>
-<a class="sourceLine" id="cb18-2" data-line-number="2"><span class="kw">names</span>(wind_cleared)</a>
-<a class="sourceLine" id="cb18-3" data-line-number="3"><span class="co">#> [1] "SE" "CP" "SS" "DWP" "CO"</span></a></code></pre></div>
-<p>To streamline the notation, extract the data from the <code>wind_cleared</code> list into its components:</p>
-<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb19-1" data-line-number="1">data_SE <-<span class="st"> </span>wind_cleared<span class="op">$</span>SE</a>
-<a class="sourceLine" id="cb19-2" data-line-number="2">data_CP <-<span class="st"> </span>wind_cleared<span class="op">$</span>CP</a>
-<a class="sourceLine" id="cb19-3" data-line-number="3">data_SS <-<span class="st"> </span>wind_cleared<span class="op">$</span>SS</a>
-<a class="sourceLine" id="cb19-4" data-line-number="4">data_DWP <-<span class="st"> </span>wind_cleared<span class="op">$</span>DWP</a>
-<a class="sourceLine" id="cb19-5" data-line-number="5">data_CO <-<span class="st"> </span>wind_cleared<span class="op">$</span>CO</a></code></pre></div>
-<div id="searcher-efficiency-and-carcass-persistence-trials" class="section level3">
-<h3>Searcher Efficiency and Carcass Persistence Trials</h3>
-<p>In searcher efficiency and carcass persistence trials, 15 trial carcasses were placed in each combination of visibility class (<code>D</code>, <code>M</code>, <code>RP</code>), season (<code>spring</code>, <code>summer</code>, <code>fall</code>), and size class (<code>lrg</code>, <code>med</code>, <code>sml</code>, <code>bat</code>). Data formats are like those of example 1:</p>
-<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb20-1" data-line-number="1"><span class="kw">head</span>(data_SE)</a>
-<a class="sourceLine" id="cb20-2" data-line-number="2"><span class="co">#> pkID Size Season Visibility s1 s2 s3 s4 s5</span></a>
-<a class="sourceLine" id="cb20-3" data-line-number="3"><span class="co">#> 1 pk1 bat spring RP 0 NA NA NA NA</span></a>
-<a class="sourceLine" id="cb20-4" data-line-number="4"><span class="co">#> 2 pk2 bat spring RP 0 0 NA NA NA</span></a>
-<a class="sourceLine" id="cb20-5" data-line-number="5"><span class="co">#> 3 pk3 bat spring RP 1 NA NA NA NA</span></a>
-<a class="sourceLine" id="cb20-6" data-line-number="6"><span class="co">#> 4 pk4 bat spring RP 1 NA NA NA NA</span></a>
-<a class="sourceLine" id="cb20-7" data-line-number="7"><span class="co">#> 5 pk5 bat spring RP 0 NA NA NA NA</span></a>
-<a class="sourceLine" id="cb20-8" data-line-number="8"><span class="co">#> 6 pk6 bat spring RP 1 NA NA NA NA</span></a>
-<a class="sourceLine" id="cb20-9" data-line-number="9"><span class="kw">head</span>(data_CP)</a>
-<a class="sourceLine" id="cb20-10" data-line-number="10"><span class="co">#> cpID Size Season Visibility LastPresent FirstAbsent</span></a>
-<a class="sourceLine" id="cb20-11" data-line-number="11"><span class="co">#> 1 cp1 bat spring RP 0.00 0.93</span></a>
-<a class="sourceLine" id="cb20-12" data-line-number="12"><span class="co">#> 2 cp2 bat spring RP 0.98 1.97</span></a>
-<a class="sourceLine" id="cb20-13" data-line-number="13"><span class="co">#> 3 cp3 bat spring RP 0.00 1.01</span></a>
-<a class="sourceLine" id="cb20-14" data-line-number="14"><span class="co">#> 4 cp4 bat spring RP 13.99 21.13</span></a>
-<a class="sourceLine" id="cb20-15" data-line-number="15"><span class="co">#> 5 cp5 bat spring RP 0.00 1.17</span></a>
-<a class="sourceLine" id="cb20-16" data-line-number="16"><span class="co">#> 6 cp6 bat spring RP 20.95 27.97</span></a></code></pre></div>
-</div>
-<div id="searcher-efficiency-modeling" class="section level3">
-<h3>Searcher Efficiency Modeling</h3>
-<p>With 36 combinations of covariate levels (3 visibilities x 3 seasons x 4 sizes) and two parameters (<code>p</code> and <code>k</code>), the number of possible models to consider for searcher efficiency is unwieldy using simple calls to <code>pkm</code>, but <code>pkm</code> has powerful model building and model selection capabilities that can be accessed via the arg list: <code>allCombos</code> and <code>sizeCol</code>, which are discussed below.</p>
-<p>When <code>allCombos = TRUE</code>, <code>pkm</code> fits the set of submodels of the given covariate combinations, including the full model, the null model, and everything in between. For example, if the parameter models are <code>p ~ Visibility * Season</code> and <code>k ~ Visibility</code>, <code>pkm</code> with <code>allCombos = TRUE</code> would fit all combinations of possible <code>p</code> models (<code>p ~ Visibility * Season</code>, <code>p ~ Visibility + Season</code>, <code>p ~ Visibility</code>, <code>p ~ Season</code>, and <code>p ~ 1</code>) and possible <code>k</code> models (<code>k ~ Visibility</code> and <code>k ~ 1</code>), or 10 models in all.</p>
-<p>Carcasses in different size classes would be expected to have different searcher efficiency and carcass persistence parameters and would even be likely to be affected by covariates in different ways. When analyzing data with carcasses in different size classes, it is recommended that separate CP and SE models be fit for size classes separately. This can be accomplished using the <code>sizeCol</code> argument in <code>pkm</code> and <code>cpm</code>, where <code>sizeCol</code> gives the name of the column that gives the carcass size classes in <code>data_SE</code>.</p>
-<div class="sourceCode" id="cb21"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb21-1" data-line-number="1">pkModels <-<span class="st"> </span><span class="kw">pkm</span>(p <span class="op">~</span><span class="st"> </span>Visibility <span class="op">*</span><span class="st"> </span>Season, k <span class="op">~</span><span class="st"> </span>Visibility <span class="op">*</span><span class="st"> </span>Season, <span class="dt">data =</span> data_SE,</a>
-<a class="sourceLine" id="cb21-2" data-line-number="2"> <span class="dt">allCombos =</span> <span class="ot">TRUE</span>, <span class="dt">sizeCol =</span> <span class="st">"Size"</span>)</a>
-<a class="sourceLine" id="cb21-3" data-line-number="3"><span class="kw">class</span>(pkModels)</a>
-<a class="sourceLine" id="cb21-4" data-line-number="4"><span class="co">#> [1] "pkmSetSize" "list"</span></a>
-<a class="sourceLine" id="cb21-5" data-line-number="5"><span class="kw">names</span>(pkModels)</a>
-<a class="sourceLine" id="cb21-6" data-line-number="6"><span class="co">#> [1] "bat" "lrg" "med" "sml"</span></a></code></pre></div>
-<p>When <code>allCombos = TRUE</code> and <code>sizeCol</code> is defined, <code>pkm</code> returns a list of sets of models for each size class. The sets of models for each size class include the full spectrum of models that can be constructed using simple combinations of the covariates.</p>
-<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb22-1" data-line-number="1"><span class="kw">names</span>(pkModels[[<span class="st">"sml"</span>]])</a>
-<a class="sourceLine" id="cb22-2" data-line-number="2"><span class="co">#> [1] "p ~ Visibility * Season; k ~ Visibility * Season" "p ~ Visibility + Season; k ~ Visibility * Season"</span></a>
-<a class="sourceLine" id="cb22-3" data-line-number="3"><span class="co">#> [3] "p ~ Season; k ~ Visibility * Season" "p ~ Visibility; k ~ Visibility * Season" </span></a>
-<a class="sourceLine" id="cb22-4" data-line-number="4"><span class="co">#> [5] "p ~ 1; k ~ Visibility * Season" "p ~ Visibility * Season; k ~ Visibility + Season"</span></a>
-<a class="sourceLine" id="cb22-5" data-line-number="5"><span class="co">#> [7] "p ~ Visibility + Season; k ~ Visibility + Season" "p ~ Season; k ~ Visibility + Season" </span></a>
-<a class="sourceLine" id="cb22-6" data-line-number="6"><span class="co">#> [9] "p ~ Visibility; k ~ Visibility + Season" "p ~ 1; k ~ Visibility + Season" </span></a>
-<a class="sourceLine" id="cb22-7" data-line-number="7"><span class="co">#> [11] "p ~ Visibility * Season; k ~ Season" "p ~ Visibility + Season; k ~ Season" </span></a>
-<a class="sourceLine" id="cb22-8" data-line-number="8"><span class="co">#> [13] "p ~ Season; k ~ Season" "p ~ Visibility; k ~ Season" </span></a>
-<a class="sourceLine" id="cb22-9" data-line-number="9"><span class="co">#> [15] "p ~ 1; k ~ Season" "p ~ Visibility * Season; k ~ Visibility" </span></a>
-<a class="sourceLine" id="cb22-10" data-line-number="10"><span class="co">#> [17] "p ~ Visibility + Season; k ~ Visibility" "p ~ Season; k ~ Visibility" </span></a>
-<a class="sourceLine" id="cb22-11" data-line-number="11"><span class="co">#> [19] "p ~ Visibility; k ~ Visibility" "p ~ 1; k ~ Visibility" </span></a>
-<a class="sourceLine" id="cb22-12" data-line-number="12"><span class="co">#> [21] "p ~ Visibility * Season; k ~ 1" "p ~ Visibility + Season; k ~ 1" </span></a>
-<a class="sourceLine" id="cb22-13" data-line-number="13"><span class="co">#> [23] "p ~ Season; k ~ 1" "p ~ Visibility; k ~ 1" </span></a>
-<a class="sourceLine" id="cb22-14" data-line-number="14"><span class="co">#> [25] "p ~ 1; k ~ 1"</span></a>
-<a class="sourceLine" id="cb22-15" data-line-number="15"><span class="kw">class</span>(pkModels[[<span class="st">"sml"</span>]])</a>
-<a class="sourceLine" id="cb22-16" data-line-number="16"><span class="co">#> [1] "pkmSet" "list"</span></a></code></pre></div>
-<p>To estimate mortality, one model for each size class must be selected from the long list of models fit. GenEst provides several tools for guiding the selection. First, the models can be listed by AICc, which gives a score for the quality of the model for the given data. Complicated models that use many parameters may fit the data more closely than a simpler model but are penalized because of their complexity and relative instability. The scores have meaning only in comparison with other models’. AICc provides a rough but useful guide for model selection, but should in no way be relied upon as definitive. Its utility is in identifying relatively poor models and in narrowing the choice of plausible models to a manageable number.</p>
-<p>The <code>aicc</code> function lists the fitted models in order of <span class="math inline">\({\small \Delta}\)</span>AICc for each size class (if applicable). For this discussion, we will focus on <code>sml</code> only, but for a full analysis, all size classes would need to be similarly analyzed.</p>
-<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb23-1" data-line-number="1"><span class="kw">aicc</span>(pkModels[[<span class="st">"sml"</span>]])</a>
-<a class="sourceLine" id="cb23-2" data-line-number="2"><span class="co">#> p Formula k Formula AICc <U+0394>AICc</span></a>
-<a class="sourceLine" id="cb23-3" data-line-number="3"><span class="co">#> 24 p ~ Visibility k ~ 1 318.55 0.00</span></a>
-<a class="sourceLine" id="cb23-4" data-line-number="4"><span class="co">#> 19 p ~ Visibility k ~ Visibility 319.69 1.14</span></a>
-<a class="sourceLine" id="cb23-5" data-line-number="5"><span class="co">#> 14 p ~ Visibility k ~ Season 321.42 2.87</span></a>
-<a class="sourceLine" id="cb23-6" data-line-number="6"><span class="co">#> 21 p ~ Visibility * Season k ~ 1 321.53 2.98</span></a>
-<a class="sourceLine" id="cb23-7" data-line-number="7"><span class="co">#> 22 p ~ Visibility + Season k ~ 1 322.17 3.62</span></a>
-<a class="sourceLine" id="cb23-8" data-line-number="8"><span class="co">#> 16 p ~ Visibility * Season k ~ Visibility 322.98 4.43</span></a>
-<a class="sourceLine" id="cb23-9" data-line-number="9"><span class="co">#> 17 p ~ Visibility + Season k ~ Visibility 323.14 4.59</span></a>
-<a class="sourceLine" id="cb23-10" data-line-number="10"><span class="co">#> 9 p ~ Visibility k ~ Visibility + Season 323.44 4.89</span></a>
-<a class="sourceLine" id="cb23-11" data-line-number="11"><span class="co">#> 11 p ~ Visibility * Season k ~ Season 323.97 5.42</span></a>
-<a class="sourceLine" id="cb23-12" data-line-number="12"><span class="co">#> 12 p ~ Visibility + Season k ~ Season 324.33 5.78</span></a>
-<a class="sourceLine" id="cb23-13" data-line-number="13"><span class="co">#> 7 p ~ Visibility + Season k ~ Visibility + Season 326.33 7.78</span></a>
-<a class="sourceLine" id="cb23-14" data-line-number="14"><span class="co">#> 6 p ~ Visibility * Season k ~ Visibility + Season 326.51 7.96</span></a>
-<a class="sourceLine" id="cb23-15" data-line-number="15"><span class="co">#> 4 p ~ Visibility k ~ Visibility * Season 327.99 9.44</span></a>
-<a class="sourceLine" id="cb23-16" data-line-number="16"><span class="co">#> 2 p ~ Visibility + Season k ~ Visibility * Season 331.07 12.52</span></a>
-<a class="sourceLine" id="cb23-17" data-line-number="17"><span class="co">#> 1 p ~ Visibility * Season k ~ Visibility * Season 333.47 14.92</span></a>
-<a class="sourceLine" id="cb23-18" data-line-number="18"><span class="co">#> 20 p ~ 1 k ~ Visibility 339.51 20.96</span></a>
-<a class="sourceLine" id="cb23-19" data-line-number="19"><span class="co">#> 10 p ~ 1 k ~ Visibility + Season 342.41 23.86</span></a>
-<a class="sourceLine" id="cb23-20" data-line-number="20"><span class="co">#> 18 p ~ Season k ~ Visibility 342.74 24.19</span></a>
-<a class="sourceLine" id="cb23-21" data-line-number="21"><span class="co">#> 8 p ~ Season k ~ Visibility + Season 344.10 25.55</span></a>
-<a class="sourceLine" id="cb23-22" data-line-number="22"><span class="co">#> 25 p ~ 1 k ~ 1 346.45 27.90</span></a>
-<a class="sourceLine" id="cb23-23" data-line-number="23"><span class="co">#> 5 p ~ 1 k ~ Visibility * Season 347.47 28.92</span></a>
-<a class="sourceLine" id="cb23-24" data-line-number="24"><span class="co">#> 15 p ~ 1 k ~ Season 347.66 29.11</span></a>
-<a class="sourceLine" id="cb23-25" data-line-number="25"><span class="co">#> 23 p ~ Season k ~ 1 350.35 31.80</span></a>
-<a class="sourceLine" id="cb23-26" data-line-number="26"><span class="co">#> 13 p ~ Season k ~ Season 350.44 31.89</span></a>
-<a class="sourceLine" id="cb23-27" data-line-number="27"><span class="co">#> 3 p ~ Season k ~ Visibility * Season 350.49 31.94</span></a></code></pre></div>
-<p>Preference should normally be given to models with <span class="math inline">\({\small \Delta}\)</span>AICc less than 6 or 7. Models with differences of less than 3 or 4 are generally considered indistinguishable by this measure. Choices among such models should be based on other criteria.</p>
-<p>Diagnostic plots can be used to identify potential problems with model fits and to help distinguish between models with similar AICc scores. The <code>plot</code> function is defined for <code>pkm</code> (one model) and <code>pkmSet</code> (set of models for a given size class) objects. For example, <code>plot(pkModels[["sml"]][[1]])</code> would produce the single figure for the first model for the <code>sml</code> size class. <code>plot(pkModels[["sml"]])</code> would create plots for each model fit for the <code>sml</code> size class. To plot a specific single model from the full set, use the <code>specificModel</code> argument. For example, diagnostic plots for the model with the lowest AICc score are shown below:</p>
-<div class="sourceCode" id="cb24"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb24-1" data-line-number="1"><span class="kw">plot</span>(pkModels[[<span class="st">"sml"</span>]], <span class="dt">specificModel =</span> <span class="st">"p ~ Visibility; k ~ 1"</span>)</a></code></pre></div>
-<p><img 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" /><!-- --></p>
-<p>The top row shows box plots of estimated <code>p</code> and <code>k</code> parameters for all cells (i.e., combinations of covariate levels, like <code>D.fall</code> for difficult visibility in the fall) for both the selected model (black) and the full model (gray). With the full model, the fits for each cell are based solely on data from that specific cell. The advantage is that each cell’s estimates are untainted by data from other cells. The disadvantage is that the sample size for each estimate is relatively small and the error bars large. In the reduced models, estimates for one cell borrow strength from estimates in related cells. This gives smaller error bars but can lead to errors if the model structure does not properly reflect the dependence of searcher efficiency parameters on cell characteristics.</p>
-<p>In the figure, the estimates of <code>p</code> from the selected model are markedly less variable than the estimates from the full model, while the locations of the boxes are very similar for the two models. Thus, this selected model (the one with the lowest AICc) appears to be an improvement over the full cell model for estimating <code>p</code>.</p>
-<p>The <span class="math inline">\({\small \Delta}\)</span>AICc for the full model is 12.52, which indicates a serious deficiency in comparison to the model with the best fit, <code>"p ~ Visibility; k ~ 1"</code>. The boxplots for <code>k</code> highlight one particular problem with the fit of the reference model. In some of the cells, the boxes extend from 0 to 1, which suggests that the reference model is unable to estimate <code>k</code> for the given cell. Selecting a simpler model for <code>k</code> often remedies this problem. If all models display this 0-1 phenomenon, a fixed <code>k</code> of 1 is appropriate if a smaller proportion of carcasses was found on the first search occasion than on later searches.</p>
-<p>By comparison, the model with the highest <span class="math inline">\({\small \Delta}\)</span>AICc (= <code>{r max(aicc(pkModels[["sml"]]))}</code> routinely estimates <code>p</code> and <code>k</code> either well above or well below the reference model but has fairly tight error bars–bad estimates but quite confident about them!</p>
-<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb25-1" data-line-number="1"><span class="kw">plot</span>(pkModels[[<span class="st">"sml"</span>]], <span class="dt">specificModel =</span> <span class="st">"p ~ Season; k ~ Visibility * Season"</span>)</a></code></pre></div>
-<p><img 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" /><!-- --></p>
-<p>A similar model selection exercise gives the same form of model (<code>p ~ Visibility; k ~ 1</code>) for large birds, medium birds, and bats. These can all be collated into a list for later analysis of detection probabilities and mortality rates.</p>
-<div class="sourceCode" id="cb26"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb26-1" data-line-number="1">pkMods <-<span class="st"> </span><span class="kw">list</span>(</a>
-<a class="sourceLine" id="cb26-2" data-line-number="2"> <span class="dt">sml =</span> pkModels[[<span class="st">"sml"</span>]][[<span class="st">"p ~ Visibility; k ~ 1"</span>]],</a>
-<a class="sourceLine" id="cb26-3" data-line-number="3"> <span class="dt">med =</span> pkModels[[<span class="st">"med"</span>]][[<span class="st">"p ~ Visibility; k ~ 1"</span>]],</a>
-<a class="sourceLine" id="cb26-4" data-line-number="4"> <span class="dt">lrg =</span> pkModels[[<span class="st">"lrg"</span>]][[<span class="st">"p ~ Visibility; k ~ 1"</span>]],</a>
-<a class="sourceLine" id="cb26-5" data-line-number="5"> <span class="dt">bat =</span> pkModels[[<span class="st">"bat"</span>]][[<span class="st">"p ~ Visibility; k ~ 1"</span>]]</a>
-<a class="sourceLine" id="cb26-6" data-line-number="6">)</a></code></pre></div>
-</div>
-<div id="carcass-persistence-modeling" class="section level3">
-<h3>Carcass Persistence Modeling</h3>
-<p>The work flow for carcass persistence modeling is similar to that for searcher efficiency except that in addition to selecting covariates for two different parameters (location = <code>l</code> and scale = <code>s</code>), there are four model forms to choose from: Weibull, lognormal, loglogistic, and exponential.</p>
-<div class="sourceCode" id="cb27"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb27-1" data-line-number="1">cpModels <-<span class="st"> </span><span class="kw">cpm</span>(</a>
-<a class="sourceLine" id="cb27-2" data-line-number="2"> l <span class="op">~</span><span class="st"> </span>Visibility <span class="op">*</span><span class="st"> </span>Season, s <span class="op">~</span><span class="st"> </span>Visibility <span class="op">*</span><span class="st"> </span>Season,</a>
-<a class="sourceLine" id="cb27-3" data-line-number="3"> <span class="dt">data =</span> data_CP, <span class="dt">left =</span> <span class="st">"LastPresent"</span>, <span class="dt">right =</span> <span class="st">"FirstAbsent"</span>,</a>
-<a class="sourceLine" id="cb27-4" data-line-number="4"> <span class="dt">dist =</span> <span class="kw">c</span>( <span class="st">"weibull"</span>, <span class="st">"lognormal"</span>, <span class="st">"loglogistic"</span>, <span class="st">"exponential"</span>),</a>
-<a class="sourceLine" id="cb27-5" data-line-number="5"> <span class="dt">allCombos =</span> <span class="ot">TRUE</span>, <span class="dt">sizeCol =</span> <span class="st">"Size"</span></a>
-<a class="sourceLine" id="cb27-6" data-line-number="6">)</a></code></pre></div>
-<p>The list of models is long:</p>
-<div class="sourceCode" id="cb28"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb28-1" data-line-number="1"><span class="kw">lapply</span>(<span class="kw">aicc</span>(cpModels), nrow)</a>
-<a class="sourceLine" id="cb28-2" data-line-number="2"><span class="co">#> $bat</span></a>
-<a class="sourceLine" id="cb28-3" data-line-number="3"><span class="co">#> [1] 80</span></a>
-<a class="sourceLine" id="cb28-4" data-line-number="4"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb28-5" data-line-number="5"><span class="co">#> $lrg</span></a>
-<a class="sourceLine" id="cb28-6" data-line-number="6"><span class="co">#> [1] 80</span></a>
-<a class="sourceLine" id="cb28-7" data-line-number="7"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb28-8" data-line-number="8"><span class="co">#> $med</span></a>
-<a class="sourceLine" id="cb28-9" data-line-number="9"><span class="co">#> [1] 80</span></a>
-<a class="sourceLine" id="cb28-10" data-line-number="10"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb28-11" data-line-number="11"><span class="co">#> $sml</span></a>
-<a class="sourceLine" id="cb28-12" data-line-number="12"><span class="co">#> [1] 80</span></a>
-<a class="sourceLine" id="cb28-13" data-line-number="13"><span class="kw">aicc</span>(cpModels[[<span class="st">"sml"</span>]])</a>
-<a class="sourceLine" id="cb28-14" data-line-number="14"><span class="co">#> Distribution Location Formula Scale Formula AICc <U+0394>AICc</span></a>
-<a class="sourceLine" id="cb28-15" data-line-number="15"><span class="co">#> 25 weibull l ~ 1 s ~ 1 780.00 0.00</span></a>
-<a class="sourceLine" id="cb28-16" data-line-number="16"><span class="co">#> 24 weibull l ~ Visibility s ~ 1 780.16 0.16</span></a>
-<a class="sourceLine" id="cb28-17" data-line-number="17"><span class="co">#> 15 weibull l ~ 1 s ~ Season 780.38 0.38</span></a>
-<a class="sourceLine" id="cb28-18" data-line-number="18"><span class="co">#> 19 weibull l ~ Visibility s ~ Visibility 780.97 0.97</span></a>
-<a class="sourceLine" id="cb28-19" data-line-number="19"><span class="co">#> 20 weibull l ~ 1 s ~ Visibility 781.32 1.32</span></a>
-<a class="sourceLine" id="cb28-20" data-line-number="20"><span class="co">#> 10 weibull l ~ 1 s ~ Visibility + Season 782.00 2.00</span></a>
-<a class="sourceLine" id="cb28-21" data-line-number="21"><span class="co">#> 14 weibull l ~ Visibility s ~ Season 782.04 2.04</span></a>
-<a class="sourceLine" id="cb28-22" data-line-number="22"><span class="co">#> 22 weibull l ~ Visibility + Season s ~ 1 782.55 2.55</span></a>
-<a class="sourceLine" id="cb28-23" data-line-number="23"><span class="co">#> 74 loglogistic l ~ Visibility s ~ 1 782.57 2.57</span></a>
-<a class="sourceLine" id="cb28-24" data-line-number="24"><span class="co">#> 21 weibull l ~ Visibility * Season s ~ 1 782.77 2.77</span></a>
-<a class="sourceLine" id="cb28-25" data-line-number="25"><span class="co">#> 49 lognormal l ~ Visibility s ~ 1 782.84 2.84</span></a>
-<a class="sourceLine" id="cb28-26" data-line-number="26"><span class="co">#> 23 weibull l ~ Season s ~ 1 782.88 2.88</span></a>
-<a class="sourceLine" id="cb28-27" data-line-number="27"><span class="co">#> 71 loglogistic l ~ Visibility * Season s ~ 1 783.06 3.06</span></a>
-<a class="sourceLine" id="cb28-28" data-line-number="28"><span class="co">#> 13 weibull l ~ Season s ~ Season 783.16 3.16</span></a>
-<a class="sourceLine" id="cb28-29" data-line-number="29"><span class="co">#> 50 lognormal l ~ 1 s ~ 1 783.18 3.18</span></a>
-<a class="sourceLine" id="cb28-30" data-line-number="30"><span class="co">#> 75 loglogistic l ~ 1 s ~ 1 783.30 3.30</span></a>
-<a class="sourceLine" id="cb28-31" data-line-number="31"><span class="co">#> 9 weibull l ~ Visibility s ~ Visibility + Season 783.33 3.33</span></a>
-<a class="sourceLine" id="cb28-32" data-line-number="32"><span class="co">#> 16 weibull l ~ Visibility * Season s ~ Visibility 783.42 3.42</span></a>
-<a class="sourceLine" id="cb28-33" data-line-number="33"><span class="co">#> 46 lognormal l ~ Visibility * Season s ~ 1 783.86 3.86</span></a>
-<a class="sourceLine" id="cb28-34" data-line-number="34"><span class="co">#> 47 lognormal l ~ Visibility + Season s ~ 1 783.86 3.86</span></a>
-<a class="sourceLine" id="cb28-35" data-line-number="35"><span class="co">#> 40 lognormal l ~ 1 s ~ Season 783.88 3.88</span></a>
-<a class="sourceLine" id="cb28-36" data-line-number="36"><span class="co">#> 11 weibull l ~ Visibility * Season s ~ Season 783.95 3.95</span></a>
-<a class="sourceLine" id="cb28-37" data-line-number="37"><span class="co">#> 72 loglogistic l ~ Visibility + Season s ~ 1 784.00 4.00</span></a>
-<a class="sourceLine" id="cb28-38" data-line-number="38"><span class="co">#> 17 weibull l ~ Visibility + Season s ~ Visibility 784.09 4.09</span></a>
-<a class="sourceLine" id="cb28-39" data-line-number="39"><span class="co">#> 48 lognormal l ~ Season s ~ 1 784.13 4.13</span></a>
-<a class="sourceLine" id="cb28-40" data-line-number="40"><span class="co">#> 69 loglogistic l ~ Visibility s ~ Visibility 784.28 4.28</span></a>
-<a class="sourceLine" id="cb28-41" data-line-number="41"><span class="co">#> 65 loglogistic l ~ 1 s ~ Season 784.30 4.30</span></a>
-<a class="sourceLine" id="cb28-42" data-line-number="42"><span class="co">#> 12 weibull l ~ Visibility + Season s ~ Season 784.48 4.48</span></a>
-<a class="sourceLine" id="cb28-43" data-line-number="43"><span class="co">#> 73 loglogistic l ~ Season s ~ 1 784.54 4.54</span></a>
-<a class="sourceLine" id="cb28-44" data-line-number="44"><span class="co">#> 44 lognormal l ~ Visibility s ~ Visibility 784.58 4.58</span></a>
-<a class="sourceLine" id="cb28-45" data-line-number="45"><span class="co">#> 18 weibull l ~ Season s ~ Visibility 784.85 4.85</span></a>
-<a class="sourceLine" id="cb28-46" data-line-number="46"><span class="co">#> 66 loglogistic l ~ Visibility * Season s ~ Visibility 784.85 4.85</span></a>
-<a class="sourceLine" id="cb28-47" data-line-number="47"><span class="co">#> 64 loglogistic l ~ Visibility s ~ Season 784.95 4.95</span></a>
-<a class="sourceLine" id="cb28-48" data-line-number="48"><span class="co">#> 39 lognormal l ~ Visibility s ~ Season 785.01 5.01</span></a>
-<a class="sourceLine" id="cb28-49" data-line-number="49"><span class="co">#> 38 lognormal l ~ Season s ~ Season 785.09 5.09</span></a>
-<a class="sourceLine" id="cb28-50" data-line-number="50"><span class="co">#> 45 lognormal l ~ 1 s ~ Visibility 785.14 5.14</span></a>
-<a class="sourceLine" id="cb28-51" data-line-number="51"><span class="co">#> 6 weibull l ~ Visibility * Season s ~ Visibility + Season 785.29 5.29</span></a>
-<a class="sourceLine" id="cb28-52" data-line-number="52"><span class="co">#> 70 loglogistic l ~ 1 s ~ Visibility 785.31 5.31</span></a>
-<a class="sourceLine" id="cb28-53" data-line-number="53"><span class="co">#> 8 weibull l ~ Season s ~ Visibility + Season 785.43 5.43</span></a>
-<a class="sourceLine" id="cb28-54" data-line-number="54"><span class="co">#> 61 loglogistic l ~ Visibility * Season s ~ Season 785.47 5.47</span></a>
-<a class="sourceLine" id="cb28-55" data-line-number="55"><span class="co">#> 41 lognormal l ~ Visibility * Season s ~ Visibility 785.68 5.68</span></a>
-<a class="sourceLine" id="cb28-56" data-line-number="56"><span class="co">#> 63 loglogistic l ~ Season s ~ Season 785.71 5.71</span></a>
-<a class="sourceLine" id="cb28-57" data-line-number="57"><span class="co">#> 36 lognormal l ~ Visibility * Season s ~ Season 785.89 5.89</span></a>
-<a class="sourceLine" id="cb28-58" data-line-number="58"><span class="co">#> 35 lognormal l ~ 1 s ~ Visibility + Season 786.07 6.07</span></a>
-<a class="sourceLine" id="cb28-59" data-line-number="59"><span class="co">#> 42 lognormal l ~ Visibility + Season s ~ Visibility 786.15 6.15</span></a>
-<a class="sourceLine" id="cb28-60" data-line-number="60"><span class="co">#> 37 lognormal l ~ Visibility + Season s ~ Season 786.29 6.29</span></a>
-<a class="sourceLine" id="cb28-61" data-line-number="61"><span class="co">#> 67 loglogistic l ~ Visibility + Season s ~ Visibility 786.30 6.30</span></a>
-<a class="sourceLine" id="cb28-62" data-line-number="62"><span class="co">#> 7 weibull l ~ Visibility + Season s ~ Visibility + Season 786.48 6.48</span></a>
-<a class="sourceLine" id="cb28-63" data-line-number="63"><span class="co">#> 43 lognormal l ~ Season s ~ Visibility 786.48 6.48</span></a>
-<a class="sourceLine" id="cb28-64" data-line-number="64"><span class="co">#> 60 loglogistic l ~ 1 s ~ Visibility + Season 786.56 6.56</span></a>
-<a class="sourceLine" id="cb28-65" data-line-number="65"><span class="co">#> 62 loglogistic l ~ Visibility + Season s ~ Season 786.56 6.56</span></a>
-<a class="sourceLine" id="cb28-66" data-line-number="66"><span class="co">#> 68 loglogistic l ~ Season s ~ Visibility 786.89 6.89</span></a>
-<a class="sourceLine" id="cb28-67" data-line-number="67"><span class="co">#> 59 loglogistic l ~ Visibility s ~ Visibility + Season 786.94 6.94</span></a>
-<a class="sourceLine" id="cb28-68" data-line-number="68"><span class="co">#> 34 lognormal l ~ Visibility s ~ Visibility + Season 787.01 7.01</span></a>
-<a class="sourceLine" id="cb28-69" data-line-number="69"><span class="co">#> 56 loglogistic l ~ Visibility * Season s ~ Visibility + Season 787.41 7.41</span></a>
-<a class="sourceLine" id="cb28-70" data-line-number="70"><span class="co">#> 33 lognormal l ~ Season s ~ Visibility + Season 787.71 7.71</span></a>
-<a class="sourceLine" id="cb28-71" data-line-number="71"><span class="co">#> 31 lognormal l ~ Visibility * Season s ~ Visibility + Season 787.87 7.87</span></a>
-<a class="sourceLine" id="cb28-72" data-line-number="72"><span class="co">#> 58 loglogistic l ~ Season s ~ Visibility + Season 788.35 8.35</span></a>
-<a class="sourceLine" id="cb28-73" data-line-number="73"><span class="co">#> 32 lognormal l ~ Visibility + Season s ~ Visibility + Season 788.83 8.83</span></a>
-<a class="sourceLine" id="cb28-74" data-line-number="74"><span class="co">#> 57 loglogistic l ~ Visibility + Season s ~ Visibility + Season 789.12 9.12</span></a>
-<a class="sourceLine" id="cb28-75" data-line-number="75"><span class="co">#> 5 weibull l ~ 1 s ~ Visibility * Season 789.98 9.98</span></a>
-<a class="sourceLine" id="cb28-76" data-line-number="76"><span class="co">#> 4 weibull l ~ Visibility s ~ Visibility * Season 791.45 11.45</span></a>
-<a class="sourceLine" id="cb28-77" data-line-number="77"><span class="co">#> 3 weibull l ~ Season s ~ Visibility * Season 793.01 13.01</span></a>
-<a class="sourceLine" id="cb28-78" data-line-number="78"><span class="co">#> 1 weibull l ~ Visibility * Season s ~ Visibility * Season 794.00 14.00</span></a>
-<a class="sourceLine" id="cb28-79" data-line-number="79"><span class="co">#> 30 lognormal l ~ 1 s ~ Visibility * Season 794.47 14.47</span></a>
-<a class="sourceLine" id="cb28-80" data-line-number="80"><span class="co">#> 2 weibull l ~ Visibility + Season s ~ Visibility * Season 794.50 14.50</span></a>
-<a class="sourceLine" id="cb28-81" data-line-number="81"><span class="co">#> 55 loglogistic l ~ 1 s ~ Visibility * Season 794.95 14.95</span></a>
-<a class="sourceLine" id="cb28-82" data-line-number="82"><span class="co">#> 29 lognormal l ~ Visibility s ~ Visibility * Season 795.13 15.13</span></a>
-<a class="sourceLine" id="cb28-83" data-line-number="83"><span class="co">#> 54 loglogistic l ~ Visibility s ~ Visibility * Season 795.14 15.14</span></a>
-<a class="sourceLine" id="cb28-84" data-line-number="84"><span class="co">#> 28 lognormal l ~ Season s ~ Visibility * Season 795.65 15.65</span></a>
-<a class="sourceLine" id="cb28-85" data-line-number="85"><span class="co">#> 53 loglogistic l ~ Season s ~ Visibility * Season 796.37 16.37</span></a>
-<a class="sourceLine" id="cb28-86" data-line-number="86"><span class="co">#> 51 loglogistic l ~ Visibility * Season s ~ Visibility * Season 796.80 16.80</span></a>
-<a class="sourceLine" id="cb28-87" data-line-number="87"><span class="co">#> 26 lognormal l ~ Visibility * Season s ~ Visibility * Season 797.20 17.20</span></a>
-<a class="sourceLine" id="cb28-88" data-line-number="88"><span class="co">#> 27 lognormal l ~ Visibility + Season s ~ Visibility * Season 797.36 17.36</span></a>
-<a class="sourceLine" id="cb28-89" data-line-number="89"><span class="co">#> 52 loglogistic l ~ Visibility + Season s ~ Visibility * Season 797.70 17.70</span></a>
-<a class="sourceLine" id="cb28-90" data-line-number="90"><span class="co">#> 76 exponential l ~ Visibility * Season NULL 835.23 55.23</span></a>
-<a class="sourceLine" id="cb28-91" data-line-number="91"><span class="co">#> 79 exponential l ~ Visibility NULL 839.78 59.78</span></a>
-<a class="sourceLine" id="cb28-92" data-line-number="92"><span class="co">#> 77 exponential l ~ Visibility + Season NULL 840.96 60.96</span></a>
-<a class="sourceLine" id="cb28-93" data-line-number="93"><span class="co">#> 80 exponential l ~ 1 NULL 842.24 62.24</span></a>
-<a class="sourceLine" id="cb28-94" data-line-number="94"><span class="co">#> 78 exponential l ~ Season NULL 844.75 64.75</span></a></code></pre></div>
-<p>It is not uncommon to see the fits for the exponential distribution at the bottom of the AIC list. The exponential distribution has only one parameter and does not have nearly as much flexibility as the others, which each have two-parameters. An implicit assumption of the exponential model is that the scavenging rate is constant, regardless of carcass age. When that assumption is not met, the exponential provides an inferior fit.</p>
-<p>To compare among a set of cp models with plausible AICs, use, for example, <code>plot(cpModels[["sml"]])</code> to browse through all the models or <code>plot(cpModels[["sml"]][["p ~ Visibility; k ~ 1"]])</code> to view a single model specified by name. It can be seen from the AICc table (<code>aicc(cpModels[["sml"]])</code>) that the top 10 models according to AICc are:</p>
-<div class="sourceCode" id="cb29"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb29-1" data-line-number="1">cp_smlCandidates <-</a>
-<a class="sourceLine" id="cb29-2" data-line-number="2"><span class="st"> </span><span class="kw">names</span>(cpModels[[<span class="st">"sml"</span>]])[<span class="kw">c</span>(<span class="dv">25</span>, <span class="dv">24</span>, <span class="dv">15</span>, <span class="dv">19</span>, <span class="dv">20</span>, <span class="dv">10</span>, <span class="dv">14</span>, <span class="dv">22</span>, <span class="dv">74</span>, <span class="dv">21</span>)]</a>
-<a class="sourceLine" id="cb29-3" data-line-number="3">cp_smlCandidates</a>
-<a class="sourceLine" id="cb29-4" data-line-number="4"><span class="co">#> [1] "dist: weibull; l ~ 1; s ~ 1" "dist: weibull; l ~ Visibility; s ~ 1" </span></a>
-<a class="sourceLine" id="cb29-5" data-line-number="5"><span class="co">#> [3] "dist: weibull; l ~ 1; s ~ Season" "dist: weibull; l ~ Visibility; s ~ Visibility"</span></a>
-<a class="sourceLine" id="cb29-6" data-line-number="6"><span class="co">#> [5] "dist: weibull; l ~ 1; s ~ Visibility" "dist: weibull; l ~ 1; s ~ Visibility + Season"</span></a>
-<a class="sourceLine" id="cb29-7" data-line-number="7"><span class="co">#> [7] "dist: weibull; l ~ Visibility; s ~ Season" "dist: weibull; l ~ Visibility + Season; s ~ 1"</span></a>
-<a class="sourceLine" id="cb29-8" data-line-number="8"><span class="co">#> [9] "dist: loglogistic; l ~ Visibility; s ~ 1" "dist: weibull; l ~ Visibility * Season; s ~ 1"</span></a></code></pre></div>
-<p>These can be compared in graphs as follows:</p>
-<div class="sourceCode" id="cb30"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb30-1" data-line-number="1"><span class="kw">plot</span>(cpModels[[<span class="st">"sml"</span>]], <span class="dt">specificModel =</span> cp_smlCandidates)</a></code></pre></div>
-<p>The figure shows the raw persistence data (fraction of carcasses remaining after the given time) for each cell, as a black stair case with Kaplan-Meier confidence intervals as dashed lines. In addition, the fitted curves for each of the distributions are shown in color, with the <code>specificModel</code> distribution having a thicker than the others. The two-parameter models (Weibull, lognormal, and loglogistic) tend to be very similar and a relatively close fit to the data. The exponential model tends to be somewhat removed from the others. Clicking on the graphing window brings up the next set of figures.</p>
-<p>We’re looking for a good fit between the selected model and the data in as many cells as possible. The first several models among the <code>cp_smlCandidates</code> seems to provide a reasonably good fit in all cells, although there seems to be a trade-off between fitting the RP.spring cell well or fitting the RP.summer cell well. <code>Visibility</code> occurs frequently in the top models, while <code>Season</code> appears more frequently in the bottom models. This indicates that <code>Season</code> is probably not a strong predictor of carcass persistence, while <code>Visibility</code> is.</p>
-<p>Selecting the top AICc model for use in mortality estimation:</p>
-<div class="sourceCode" id="cb31"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb31-1" data-line-number="1">cp_sml <-<span class="st"> </span>cpModels[[<span class="st">"sml"</span>]][[cp_smlCandidates[<span class="dv">1</span>]]]</a></code></pre></div>
-<p>Following a similar model selection process for the other size classes, we select the following:</p>
-<div class="sourceCode" id="cb32"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb32-1" data-line-number="1">cp_med <-<span class="st"> </span>cpModels[[<span class="st">"med"</span>]][[<span class="st">"dist: weibull; l ~ Visibility; s ~ Season"</span>]]</a>
-<a class="sourceLine" id="cb32-2" data-line-number="2">cp_lrg <-<span class="st"> </span>cpModels[[<span class="st">"lrg"</span>]][[<span class="st">"dist: exponential; l ~ Visibility + Season; NULL"</span>]]</a>
-<a class="sourceLine" id="cb32-3" data-line-number="3">cp_bat <-<span class="st"> </span>cpModels[[<span class="st">"bat"</span>]][[<span class="st">"dist: weibull; l ~ Visibility + Season; s ~ 1"</span>]]</a></code></pre></div>
-<p>NOTE: For large carcasses, the exponential distribution was at the top of the AICc list. The exponential requires only one parameter (<code>l</code>), so no scale parameter is provided in the model.</p>
-<p>Again, we collate the models into a list for later analysis of detection probabilities and mortality rates.</p>
-<div class="sourceCode" id="cb33"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb33-1" data-line-number="1">cpMods <-<span class="st"> </span><span class="kw">list</span>(</a>
-<a class="sourceLine" id="cb33-2" data-line-number="2"> <span class="dt">sml =</span> cp_sml,</a>
-<a class="sourceLine" id="cb33-3" data-line-number="3"> <span class="dt">med =</span> cp_med,</a>
-<a class="sourceLine" id="cb33-4" data-line-number="4"> <span class="dt">lrg =</span> cp_lrg,</a>
-<a class="sourceLine" id="cb33-5" data-line-number="5"> <span class="dt">bat =</span> cp_bat</a>
-<a class="sourceLine" id="cb33-6" data-line-number="6">)</a></code></pre></div>
-</div>
-<div id="mortality-estimation-1" class="section level3">
-<h3>Mortality Estimation</h3>
-<p>Each carcass’s contribution to the total mortality in each search interval is estimated using the <code>estM</code> function. The function call is largely similar that used in the simple scenario discussed in example 1. However, there are some important differences. First, <code>model_SE</code> and <code>model_CP</code> are lists of models, one element for each size class. In addition, the name of the size class variable is provided as <code>sizeCol = "Size"</code>. Finally, the <code>frac</code> argument represents the sampling fraction or the fraction of carcasses expected to fall at the units that were searched. In this example, 23 out of 100 turbines were searched, and <code>frac</code> is set equal to 0.23 under the assumption that the mortality rates at the unsearched turbines did not differ substantially from the rates at the searched turbines.</p>
-<div class="sourceCode" id="cb34"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb34-1" data-line-number="1">Mhat <-<span class="st"> </span><span class="kw">estM</span>(<span class="dt">nsim =</span> <span class="dv">1000</span>, <span class="dt">data_CO =</span> data_CO, <span class="dt">data_SS =</span> data_SS, <span class="dt">frac =</span> <span class="fl">0.23</span>,</a>
-<a class="sourceLine" id="cb34-2" data-line-number="2"> <span class="dt">data_DWP =</span> data_DWP, <span class="dt">model_SE =</span> pkMods, <span class="dt">model_CP =</span> cpMods,</a>
-<a class="sourceLine" id="cb34-3" data-line-number="3"> <span class="dt">sizeCol =</span> <span class="st">"Size"</span>, <span class="dt">unitCol =</span> <span class="st">"Turbine"</span>, <span class="dt">COdate =</span> <span class="st">"DateFound"</span>)</a>
-<a class="sourceLine" id="cb34-4" data-line-number="4"></a>
-<a class="sourceLine" id="cb34-5" data-line-number="5"><span class="kw">summary</span>(Mhat)</a>
-<a class="sourceLine" id="cb34-6" data-line-number="6"><span class="co">#> median 5% 95% </span></a>
-<a class="sourceLine" id="cb34-7" data-line-number="7"><span class="co">#> 1941.05 1515.35 2507.70</span></a>
-<a class="sourceLine" id="cb34-8" data-line-number="8"><span class="kw">plot</span>(Mhat)</a></code></pre></div>
-<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
-<p>This estimate is for the total number of fatalities among all size classes combined, from hummingbirds and bats to eagles and may be too vague to be very useful. Fortunately, mortality estimates may be partitioned or split into desired categories, such as species, size, or season. Splits may be performed according to characteristics of the carcasses or where they were found (e.g., species, turbine or other variable found in <code>data_CO</code>) or when they were found (e.g., season or other variable associated with search schedule and found in <code>data_SS</code>, or a vector of specific times).</p>
-<p>Carcasses were categorized not only by size but also by species, species group, the type of turbine they were found at, the visibility class of the ground where they were found, and distance from nearest turbine.</p>
-<p>Although species groups may extend across different size classes (e.g., <code>raptors</code> could include kestrels, red-tailed hawks, and golden eagles; <code>passerines</code> could include sparrows and ravens), splits according to species group can easily be accomplished:</p>
-<div class="sourceCode" id="cb35"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb35-1" data-line-number="1">M_speciesGroup <-<span class="st"> </span><span class="kw">calcSplits</span>(<span class="dt">M =</span> Mhat,</a>
-<a class="sourceLine" id="cb35-2" data-line-number="2"> <span class="dt">split_CO =</span> <span class="st">"SpeciesGroup"</span>, <span class="dt">data_CO =</span> data_CO)</a>
-<a class="sourceLine" id="cb35-3" data-line-number="3"><span class="kw">summary</span>(M_speciesGroup)</a>
-<a class="sourceLine" id="cb35-4" data-line-number="4"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb35-5" data-line-number="5"><span class="co">#> bat0 4 17.27533 45.55746 68.41916 94.88886 143.8607</span></a>
-<a class="sourceLine" id="cb35-6" data-line-number="6"><span class="co">#> bat1 49 804.85393 1000.63995 1132.55674 1273.39149 1530.0287</span></a>
-<a class="sourceLine" id="cb35-7" data-line-number="7"><span class="co">#> brd1 19 308.67903 435.81917 541.81933 650.03570 900.9340</span></a>
-<a class="sourceLine" id="cb35-8" data-line-number="8"><span class="co">#> brd2 10 68.61131 105.69084 139.10059 172.60904 228.8334</span></a>
-<a class="sourceLine" id="cb35-9" data-line-number="9"><span class="co">#> brd3 5 20.30734 40.13608 55.78144 72.07612 101.1133</span></a>
-<a class="sourceLine" id="cb35-10" data-line-number="10"><span class="co">#> attr(,"class")</span></a>
-<a class="sourceLine" id="cb35-11" data-line-number="11"><span class="co">#> [1] "splitSummary"</span></a>
-<a class="sourceLine" id="cb35-12" data-line-number="12"><span class="co">#> attr(,"CL")</span></a>
-<a class="sourceLine" id="cb35-13" data-line-number="13"><span class="co">#> [1] 0.9</span></a>
-<a class="sourceLine" id="cb35-14" data-line-number="14"><span class="co">#> attr(,"vars")</span></a>
-<a class="sourceLine" id="cb35-15" data-line-number="15"><span class="co">#> [1] "SpeciesGroup"</span></a>
-<a class="sourceLine" id="cb35-16" data-line-number="16"><span class="co">#> attr(,"type")</span></a>
-<a class="sourceLine" id="cb35-17" data-line-number="17"><span class="co">#> [1] "CO"</span></a>
-<a class="sourceLine" id="cb35-18" data-line-number="18"><span class="kw">plot</span>(M_speciesGroup)</a></code></pre></div>
-<p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
-<p>Split by species and season:</p>
-<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb36-1" data-line-number="1">M_speciesseason <-<span class="st"> </span><span class="kw">calcSplits</span>(<span class="dt">M =</span> Mhat,</a>
-<a class="sourceLine" id="cb36-2" data-line-number="2"> <span class="dt">split_CO =</span> <span class="st">"Species"</span>, <span class="dt">data_CO =</span> data_CO, <span class="dt">split_SS =</span> <span class="st">"Season"</span>, <span class="dt">data_SS =</span> data_SS)</a>
-<a class="sourceLine" id="cb36-3" data-line-number="3"><span class="kw">summary</span>(M_speciesseason)</a>
-<a class="sourceLine" id="cb36-4" data-line-number="4"><span class="co">#> $BA</span></a>
-<a class="sourceLine" id="cb36-5" data-line-number="5"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-6" data-line-number="6"><span class="co">#> spring 7.000 80.49152 145.12578 194.36649 258.5245 367.7461</span></a>
-<a class="sourceLine" id="cb36-7" data-line-number="7"><span class="co">#> summer 3.308 16.01339 50.04051 77.65476 114.5434 175.1377</span></a>
-<a class="sourceLine" id="cb36-8" data-line-number="8"><span class="co">#> fall 22.692 269.31946 361.47219 419.47888 498.3343 610.3344</span></a>
-<a class="sourceLine" id="cb36-9" data-line-number="9"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-10" data-line-number="10"><span class="co">#> $BB</span></a>
-<a class="sourceLine" id="cb36-11" data-line-number="11"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-12" data-line-number="12"><span class="co">#> spring 3.000 23.07146 70.859836 120.43639 174.7039 274.67469</span></a>
-<a class="sourceLine" id="cb36-13" data-line-number="13"><span class="co">#> summer 1.141 1.14100 8.838758 25.23335 50.0167 90.84171</span></a>
-<a class="sourceLine" id="cb36-14" data-line-number="14"><span class="co">#> fall 6.859 58.44463 96.128259 130.48905 177.0124 235.17878</span></a>
-<a class="sourceLine" id="cb36-15" data-line-number="15"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-16" data-line-number="16"><span class="co">#> $BC</span></a>
-<a class="sourceLine" id="cb36-17" data-line-number="17"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-18" data-line-number="18"><span class="co">#> spring 1.0238462 1.0238462 10.217518 34.44206 64.28466 124.51117</span></a>
-<a class="sourceLine" id="cb36-19" data-line-number="19"><span class="co">#> summer 0.9791538 0.9791538 5.582265 19.56802 36.78200 67.77804</span></a>
-<a class="sourceLine" id="cb36-20" data-line-number="20"><span class="co">#> fall 2.9970000 9.1728021 30.877900 51.93892 77.49943 125.13037</span></a>
-<a class="sourceLine" id="cb36-21" data-line-number="21"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-22" data-line-number="22"><span class="co">#> $BD</span></a>
-<a class="sourceLine" id="cb36-23" data-line-number="23"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-24" data-line-number="24"><span class="co">#> spring 0 0 0.00000 0.00000 0.00000 0.00000</span></a>
-<a class="sourceLine" id="cb36-25" data-line-number="25"><span class="co">#> summer 0 0 0.00000 0.00000 0.00000 0.00000</span></a>
-<a class="sourceLine" id="cb36-26" data-line-number="26"><span class="co">#> fall 2 2 17.44916 32.83821 52.88567 96.17814</span></a>
-<a class="sourceLine" id="cb36-27" data-line-number="27"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-28" data-line-number="28"><span class="co">#> $BE</span></a>
-<a class="sourceLine" id="cb36-29" data-line-number="29"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-30" data-line-number="30"><span class="co">#> spring 0 0 0.00000 0.00000 0.00000 0.00000</span></a>
-<a class="sourceLine" id="cb36-31" data-line-number="31"><span class="co">#> summer 0 0 0.00000 0.00000 0.00000 0.00000</span></a>
-<a class="sourceLine" id="cb36-32" data-line-number="32"><span class="co">#> fall 2 2 16.61984 31.34326 49.32105 77.97828</span></a>
-<a class="sourceLine" id="cb36-33" data-line-number="33"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-34" data-line-number="34"><span class="co">#> $LA</span></a>
-<a class="sourceLine" id="cb36-35" data-line-number="35"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-36" data-line-number="36"><span class="co">#> spring 2 2 10.08567 17.23428 25.83277 40.06849</span></a>
-<a class="sourceLine" id="cb36-37" data-line-number="37"><span class="co">#> summer 0 0 0.00000 0.00000 0.00000 0.00000</span></a>
-<a class="sourceLine" id="cb36-38" data-line-number="38"><span class="co">#> fall 2 2 11.26053 19.89534 31.21825 49.30545</span></a>
-<a class="sourceLine" id="cb36-39" data-line-number="39"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-40" data-line-number="40"><span class="co">#> $LB</span></a>
-<a class="sourceLine" id="cb36-41" data-line-number="41"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-42" data-line-number="42"><span class="co">#> spring 1 1 3.038478 8.717036 17.28596 29.32279</span></a>
-<a class="sourceLine" id="cb36-43" data-line-number="43"><span class="co">#> summer 0 0 0.000000 0.000000 0.00000 0.00000</span></a>
-<a class="sourceLine" id="cb36-44" data-line-number="44"><span class="co">#> fall 0 0 0.000000 0.000000 0.00000 0.00000</span></a>
-<a class="sourceLine" id="cb36-45" data-line-number="45"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-46" data-line-number="46"><span class="co">#> $LC</span></a>
-<a class="sourceLine" id="cb36-47" data-line-number="47"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-48" data-line-number="48"><span class="co">#> spring 1.0123 1.0123 2.999884 8.477447 16.38443 27.40671</span></a>
-<a class="sourceLine" id="cb36-49" data-line-number="49"><span class="co">#> summer 0.9877 0.9877 2.569007 8.293703 14.82151 26.66504</span></a>
-<a class="sourceLine" id="cb36-50" data-line-number="50"><span class="co">#> fall 0.0000 0.0000 0.000000 0.000000 0.00000 0.00000</span></a>
-<a class="sourceLine" id="cb36-51" data-line-number="51"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-52" data-line-number="52"><span class="co">#> $LE</span></a>
-<a class="sourceLine" id="cb36-53" data-line-number="53"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-54" data-line-number="54"><span class="co">#> spring 0.07 0.07 0.070000 0.070000 0.07000 3.948398</span></a>
-<a class="sourceLine" id="cb36-55" data-line-number="55"><span class="co">#> summer 0.93 0.93 1.686829 7.305527 13.76744 24.405460</span></a>
-<a class="sourceLine" id="cb36-56" data-line-number="56"><span class="co">#> fall 0.00 0.00 0.000000 0.000000 0.00000 0.000000</span></a>
-<a class="sourceLine" id="cb36-57" data-line-number="57"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-58" data-line-number="58"><span class="co">#> $MA</span></a>
-<a class="sourceLine" id="cb36-59" data-line-number="59"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-60" data-line-number="60"><span class="co">#> spring 1.002 1.002 5.261638 15.74294 29.39191 51.93297</span></a>
-<a class="sourceLine" id="cb36-61" data-line-number="61"><span class="co">#> summer 0.999 0.999 3.239589 10.73339 19.72536 35.91702</span></a>
-<a class="sourceLine" id="cb36-62" data-line-number="62"><span class="co">#> fall 0.999 0.999 3.618257 11.25274 20.13663 37.21596</span></a>
-<a class="sourceLine" id="cb36-63" data-line-number="63"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-64" data-line-number="64"><span class="co">#> $MB</span></a>
-<a class="sourceLine" id="cb36-65" data-line-number="65"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-66" data-line-number="66"><span class="co">#> spring 0.000 0.000 0.000000 0.0000 0.00000 0.00000</span></a>
-<a class="sourceLine" id="cb36-67" data-line-number="67"><span class="co">#> summer 0.018 0.018 0.018000 0.0180 0.01800 0.01800</span></a>
-<a class="sourceLine" id="cb36-68" data-line-number="68"><span class="co">#> fall 0.982 0.982 2.940807 10.3708 18.78465 36.42466</span></a>
-<a class="sourceLine" id="cb36-69" data-line-number="69"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-70" data-line-number="70"><span class="co">#> $MC</span></a>
-<a class="sourceLine" id="cb36-71" data-line-number="71"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-72" data-line-number="72"><span class="co">#> spring 1.253 1.253 6.901045 18.845298 33.69799 62.84581</span></a>
-<a class="sourceLine" id="cb36-73" data-line-number="73"><span class="co">#> summer 0.747 0.747 0.747000 8.600169 25.93104 57.73667</span></a>
-<a class="sourceLine" id="cb36-74" data-line-number="74"><span class="co">#> fall 0.000 0.000 0.000000 0.000000 0.00000 0.00000</span></a>
-<a class="sourceLine" id="cb36-75" data-line-number="75"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-76" data-line-number="76"><span class="co">#> $MD</span></a>
-<a class="sourceLine" id="cb36-77" data-line-number="77"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-78" data-line-number="78"><span class="co">#> spring 1 1 3.944607 11.94654 23.09855 40.69314</span></a>
-<a class="sourceLine" id="cb36-79" data-line-number="79"><span class="co">#> summer 0 0 0.000000 0.00000 0.00000 0.00000</span></a>
-<a class="sourceLine" id="cb36-80" data-line-number="80"><span class="co">#> fall 0 0 0.000000 0.00000 0.00000 0.00000</span></a>
-<a class="sourceLine" id="cb36-81" data-line-number="81"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-82" data-line-number="82"><span class="co">#> $ME</span></a>
-<a class="sourceLine" id="cb36-83" data-line-number="83"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-84" data-line-number="84"><span class="co">#> spring 0.000 0.000 0.000000 0.000000 0.0000 0.00000</span></a>
-<a class="sourceLine" id="cb36-85" data-line-number="85"><span class="co">#> summer 0.007 0.007 0.007000 0.007000 0.0070 0.00700</span></a>
-<a class="sourceLine" id="cb36-86" data-line-number="86"><span class="co">#> fall 0.993 0.993 2.777163 9.038206 18.6516 33.10661</span></a>
-<a class="sourceLine" id="cb36-87" data-line-number="87"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-88" data-line-number="88"><span class="co">#> $MF</span></a>
-<a class="sourceLine" id="cb36-89" data-line-number="89"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-90" data-line-number="90"><span class="co">#> spring 0 0.000000 0.00000 0.00000 0.00000 0.00</span></a>
-<a class="sourceLine" id="cb36-91" data-line-number="91"><span class="co">#> summer 0 0.000000 0.00000 0.00000 0.00000 0.00</span></a>
-<a class="sourceLine" id="cb36-92" data-line-number="92"><span class="co">#> fall 3 6.999079 26.97249 44.45388 62.31293 98.24</span></a>
-<a class="sourceLine" id="cb36-93" data-line-number="93"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-94" data-line-number="94"><span class="co">#> $MH</span></a>
-<a class="sourceLine" id="cb36-95" data-line-number="95"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-96" data-line-number="96"><span class="co">#> spring 1 1 3.211492 12.12997 22.99417 39.93869</span></a>
-<a class="sourceLine" id="cb36-97" data-line-number="97"><span class="co">#> summer 1 1 3.553370 10.66511 19.56229 35.63633</span></a>
-<a class="sourceLine" id="cb36-98" data-line-number="98"><span class="co">#> fall 0 0 0.000000 0.00000 0.00000 0.00000</span></a>
-<a class="sourceLine" id="cb36-99" data-line-number="99"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-100" data-line-number="100"><span class="co">#> $SA</span></a>
-<a class="sourceLine" id="cb36-101" data-line-number="101"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-102" data-line-number="102"><span class="co">#> spring 1 1 11.08287 34.81383 64.86929 131.8094</span></a>
-<a class="sourceLine" id="cb36-103" data-line-number="103"><span class="co">#> summer 0 0 0.00000 0.00000 0.00000 0.0000</span></a>
-<a class="sourceLine" id="cb36-104" data-line-number="104"><span class="co">#> fall 0 0 0.00000 0.00000 0.00000 0.0000</span></a>
-<a class="sourceLine" id="cb36-105" data-line-number="105"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-106" data-line-number="106"><span class="co">#> $SB</span></a>
-<a class="sourceLine" id="cb36-107" data-line-number="107"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-108" data-line-number="108"><span class="co">#> spring 0.458 0.458 0.458 0.458 29.10310 97.42842</span></a>
-<a class="sourceLine" id="cb36-109" data-line-number="109"><span class="co">#> summer 0.542 0.542 0.542 0.542 27.80569 81.84961</span></a>
-<a class="sourceLine" id="cb36-110" data-line-number="110"><span class="co">#> fall 0.000 0.000 0.000 0.000 0.00000 0.00000</span></a>
-<a class="sourceLine" id="cb36-111" data-line-number="111"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-112" data-line-number="112"><span class="co">#> $SC</span></a>
-<a class="sourceLine" id="cb36-113" data-line-number="113"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-114" data-line-number="114"><span class="co">#> spring 3 18.493 63.77301 103.6229 152.7401 243.223</span></a>
-<a class="sourceLine" id="cb36-115" data-line-number="115"><span class="co">#> summer 0 0.000 0.00000 0.0000 0.0000 0.000</span></a>
-<a class="sourceLine" id="cb36-116" data-line-number="116"><span class="co">#> fall 0 0.000 0.00000 0.0000 0.0000 0.000</span></a>
-<a class="sourceLine" id="cb36-117" data-line-number="117"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-118" data-line-number="118"><span class="co">#> $SD</span></a>
-<a class="sourceLine" id="cb36-119" data-line-number="119"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-120" data-line-number="120"><span class="co">#> spring 1.1433846 1.1433846 8.612771 25.41048 46.86664 99.14456</span></a>
-<a class="sourceLine" id="cb36-121" data-line-number="121"><span class="co">#> summer 0.8566154 0.8566154 3.533951 23.41741 50.56047 98.06769</span></a>
-<a class="sourceLine" id="cb36-122" data-line-number="122"><span class="co">#> fall 0.0000000 0.0000000 0.000000 0.00000 0.00000 0.00000</span></a>
-<a class="sourceLine" id="cb36-123" data-line-number="123"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-124" data-line-number="124"><span class="co">#> $SE</span></a>
-<a class="sourceLine" id="cb36-125" data-line-number="125"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-126" data-line-number="126"><span class="co">#> spring 0.002 0.002 0.002000 0.00200 0.00200 0.00200</span></a>
-<a class="sourceLine" id="cb36-127" data-line-number="127"><span class="co">#> summer 0.998 0.998 8.220787 27.07842 53.12961 99.80477</span></a>
-<a class="sourceLine" id="cb36-128" data-line-number="128"><span class="co">#> fall 0.000 0.000 0.000000 0.00000 0.00000 0.00000</span></a>
-<a class="sourceLine" id="cb36-129" data-line-number="129"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-130" data-line-number="130"><span class="co">#> $SG</span></a>
-<a class="sourceLine" id="cb36-131" data-line-number="131"><span class="co">#> X 5% 25% 50% 75% 95%</span></a>
-<a class="sourceLine" id="cb36-132" data-line-number="132"><span class="co">#> spring 3.042687 20.556819 65.99655 112.03281 164.43144 268.6525</span></a>
-<a class="sourceLine" id="cb36-133" data-line-number="133"><span class="co">#> summer 1.957313 1.957313 30.68359 57.91338 96.95615 173.5601</span></a>
-<a class="sourceLine" id="cb36-134" data-line-number="134"><span class="co">#> fall 0.000000 0.000000 0.00000 0.00000 0.00000 0.0000</span></a>
-<a class="sourceLine" id="cb36-135" data-line-number="135"><span class="co">#> </span></a>
-<a class="sourceLine" id="cb36-136" data-line-number="136"><span class="co">#> attr(,"class")</span></a>
-<a class="sourceLine" id="cb36-137" data-line-number="137"><span class="co">#> [1] "splitSummary"</span></a>
-<a class="sourceLine" id="cb36-138" data-line-number="138"><span class="co">#> attr(,"CL")</span></a>
-<a class="sourceLine" id="cb36-139" data-line-number="139"><span class="co">#> [1] 0.9</span></a>
-<a class="sourceLine" id="cb36-140" data-line-number="140"><span class="co">#> attr(,"vars")</span></a>
-<a class="sourceLine" id="cb36-141" data-line-number="141"><span class="co">#> [1] "Season" "Species"</span></a>
-<a class="sourceLine" id="cb36-142" data-line-number="142"><span class="co">#> attr(,"type")</span></a>
-<a class="sourceLine" id="cb36-143" data-line-number="143"><span class="co">#> [1] "SS" "CO"</span></a>
-<a class="sourceLine" id="cb36-144" data-line-number="144"><span class="co">#> attr(,"times")</span></a>
-<a class="sourceLine" id="cb36-145" data-line-number="145"><span class="co">#> [1] 0 60 130 200</span></a>
-<a class="sourceLine" id="cb36-146" data-line-number="146"><span class="kw">plot</span>(M_speciesseason)</a></code></pre></div>
-<p><img 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" style="display: block; margin: auto;" /></p>
-<p>There are so many vertical panels that it is difficult to glean any useful information out of the graph. However, the panels may be transposed and graphed for better interpretability:</p>
-<div class="sourceCode" id="cb37"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb37-1" data-line-number="1"><span class="kw">plot</span>(<span class="kw">transposeSplits</span>(M_speciesseason))</a></code></pre></div>
-<p><img 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" style="display: block; margin: auto;" /></p>
-</div>
-</div>
-
-
-
-<!-- dynamically load mathjax for compatibility with self-contained -->
-<script>
- (function () {
- var script = document.createElement("script");
- script.type = "text/javascript";
- script.src = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
- document.getElementsByTagName("head")[0].appendChild(script);
- })();
-</script>
-
-</body>
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diff --git a/man/GenEstServer.Rd b/man/GenEstServer.Rd
index a538d6d9b797a815ec5cf3795e46eebbf52663ea..6fddcd64ccdebd3ec7a83ec7e01f45b8be7a03af 100644
--- a/man/GenEstServer.Rd
+++ b/man/GenEstServer.Rd
@@ -32,7 +32,7 @@ eventReaction(eventName, rv, input, output, session)
"outSEclass", "outSEp", "outSEk", "ltp", "fta", "predsCP", "run_CP",
"run_CP_clear", "outCPclass", "outCPdist", "outCPl", "outCPs",
"run_M", "run_M_clear", "split_M", "split_M_clear", "transpose_split",
-"useSSdata", "useSSinputs", "run_g", "run_g_clear", or "outgclass".}
+"run_g", "run_g_clear", or "outgclass".}
\item{rv}{Reactive values list for the GenEst GUI.}
}
diff --git a/man/downloadData.Rd b/man/downloadData.Rd
index b6cf548e092e78692731836745fb2c8cb51837f3..a2851651ddb48fb1d4452e66bdcd0ad70db85cb2 100644
--- a/man/downloadData.Rd
+++ b/man/downloadData.Rd
@@ -9,7 +9,7 @@ downloadData(set, csvformat = NULL)
\arguments{
\item{set}{the name of the data set to download}
-\item{csvformat}{Format of .csv files to download. For comma field
+\item{csvformat}{Format of .csv files to download. For comma field
separator and period decimal separator, use \code{csvformat = NULL} or "".
For semicolon field separator and comma decimal separator, use
\code{csvformat = 2}.}
diff --git a/man/downloadMFig.Rd b/man/downloadMFig.Rd
index 776c923a6136e7f99df3f7a9358054629f0c6390..f009abec48039b0e4e1e71f9c53db708a23d44ff 100644
--- a/man/downloadMFig.Rd
+++ b/man/downloadMFig.Rd
@@ -4,7 +4,7 @@
\alias{downloadMFig}
\title{Download the M figure}
\usage{
-downloadMFig(rv, split = TRUE, transpose = FALSE)
+downloadMFig(rv, split = TRUE)
}
\arguments{
\item{rv}{the reactive values list}
diff --git a/man/modelInputWidget.Rd b/man/modelInputWidget.Rd
index d94b99ea76a0ec6a94877ea4691762d941ba26df..a060cc8a67efe6342d791a60fd1ab11efa2f833f 100644
--- a/man/modelInputWidget.Rd
+++ b/man/modelInputWidget.Rd
@@ -9,8 +9,8 @@ modelInputWidget(inType)
\arguments{
\item{inType}{Toggle control for the input type of the widget. One of
"nsim", "CL", "class", "obsSE", "predsSE", "kFixed", "ltp", "fta",
-"predsCP", "dist", "frac", "DWPCol", "COdate", "gSearchInterval",
-"gSearchMax", "useSSinputs", or "useSSdata".}
+"predsCP", "dist", "frac", "DWPCol", "COdate", "gSearchInterval", or
+"gSearchMax".}
}
\value{
HTML for the model input widget.
diff --git a/man/update_input.Rd b/man/update_input.Rd
index de4d08b0fc2ce5bd5ed2dcf16572d77d08fbf54b..fc98cfe67bb9db7fb1b8c9c821d661922d67ee48 100644
--- a/man/update_input.Rd
+++ b/man/update_input.Rd
@@ -14,7 +14,7 @@ update_input(eventName, rv, input, session)
"outSEclass", "outSEp", "outSEk", "ltp", "fta", "predsCP", "run_CP",
"run_CP_clear", "outCPclass", "outCPdist", "outCPl", "outCPs",
"run_M", "run_M_clear", "split_M", "split_M_clear", "transpose_split",
-"useSSdata", "useSSinputs", "run_g", "run_g_clear", or "outgclass".}
+"run_g", "run_g_clear", or "outgclass".}
\item{rv}{Reactive values list for the GenEst GUI.}
diff --git a/man/update_output.Rd b/man/update_output.Rd
index 78c0f6d73f4a500c08e190037a661fbbb06a1b3d..0f1e2f181b4ea21ac14b545289d7087718df5eb2 100644
--- a/man/update_output.Rd
+++ b/man/update_output.Rd
@@ -14,7 +14,7 @@ update_output(eventName, rv, output)
"outSEclass", "outSEp", "outSEk", "ltp", "fta", "predsCP", "run_CP",
"run_CP_clear", "outCPclass", "outCPdist", "outCPl", "outCPs",
"run_M", "run_M_clear", "split_M", "split_M_clear", "transpose_split",
-"useSSdata", "useSSinputs", "run_g", "run_g_clear", or "outgclass".}
+"run_g", "run_g_clear", or "outgclass".}
\item{rv}{Reactive values list for the GenEst GUI.}
diff --git a/man/update_rv.Rd b/man/update_rv.Rd
index b01d2e1910fac535b25acb7fd28ced6296a2fed6..ffb6b75b0d2685ca729f8ff869dcbdf8e973f358 100644
--- a/man/update_rv.Rd
+++ b/man/update_rv.Rd
@@ -14,9 +14,8 @@ update_rv(eventName, rv, input)
"outSEclass", "outSEp", "outSEk", "ltp", "fta", "predsCP", "run_CP",
"run_CP_clear", "outCPclass", "outCPdist", "outCPl", "outCPs",
"run_M", "run_M_clear", "split_M", "split_M_clear", "transpose_split",
-"useSSdata", "useSSinputs", "run_g", "run_g_clear", "outgclass", "load_RP",
-"load_RPbat", "load_cleared", "load_PV", "load_trough", "load_powerTower",
-or "load_mock"}
+"run_g", "run_g_clear", "outgclass", "load_RP", "load_RPbat",
+"load_cleared", "load_PV", "load_trough", "load_powerTower", or "load_mock"}
\item{rv}{Reactive values list for the GenEst GUI, created by
\code{\link{initialReactiveValues}}, which calls
diff --git a/tests/testthat/test-01-data-load.R b/tests/testthat/test-01-data-load.R
index 6a60f26178a62a53c43078d348ed8994d27834b4..4805c0a432f4fdbf51b9d7f4650785a59ba8ab67 100644
--- a/tests/testthat/test-01-data-load.R
+++ b/tests/testthat/test-01-data-load.R
@@ -68,4 +68,5 @@ test_that("Check solar_trough data", {
expect_is(solar_trough$SS, "data.frame")
expect_is(solar_trough$DWP, "data.frame")
expect_is(solar_trough$CO, "data.frame")
-})
\ No newline at end of file
+})
+
diff --git a/tests/testthat/test-02-GUI-UI.R b/tests/testthat/test-02-GUI-UI.R
index eb91b7947d7ec6f30436ddfe83c3478693d4a3a6..6f3c4b1b8752a16934b8b6faa1b694ee343bea16 100644
--- a/tests/testthat/test-02-GUI-UI.R
+++ b/tests/testthat/test-02-GUI-UI.R
@@ -74,8 +74,6 @@ test_that("Standard widget functions produce HTML", {
expect_is(modelInputWidget("COdate"), "shiny.tag")
expect_is(modelInputWidget("gSearchInterval"), "shiny.tag")
expect_is(modelInputWidget("gSearchMax"), "shiny.tag")
- expect_is(modelInputWidget("useSSinputs"), "shiny.tag")
- expect_is(modelInputWidget("useSSdata"), "shiny.tag")
expect_error(modelInputWidget("_NOT_RIGHT_"))
expect_is(modelRunWidget("SE")[[1]], "shiny.tag")
expect_is(modelRunWidget("SE")[[2]], "shiny.tag")
diff --git a/vignettes/GenEstGUI.Rmd b/vignettes/GenEstGUI.Rmd
index 7e62053c279d45912b3ddf7abc74ebdef3350052..e3911ae0e01730a1f59ce2c27c924b1735ae4e0b 100644
--- a/vignettes/GenEstGUI.Rmd
+++ b/vignettes/GenEstGUI.Rmd
@@ -6,7 +6,7 @@ output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{GenEst - 4. Graphic User Interface}
%\VignetteEngine{knitr::rmarkdown}
- \usepackage[utf8]{inputenc}
+ %\usepackage[utf8]{inputenc}
---
# Overview
diff --git a/vignettes/command-line-example.Rmd b/vignettes/command-line-example.Rmd
index 81110cb440f3c14305b4f7bf40ffea5ce77c5bf8..a0a5125f31e23718c4619aa4dc0ba16b9ac7e951 100644
--- a/vignettes/command-line-example.Rmd
+++ b/vignettes/command-line-example.Rmd
@@ -6,7 +6,7 @@ output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{GenEst - 3. Command Line Walkthrough}
%\VignetteEngine{knitr::rmarkdown}
- \usepackage[utf8]{inputenc}
+ %\usepackage[utf8]{inputenc}
---
```{r setup, include=FALSE}
diff --git a/vignettes/solar-examples.Rmd b/vignettes/solar-examples.Rmd
index e22702fc66cf3747d526b5f7b53578c7574b5171..bc9fb9aceefeeb1ff86ee478579483863086eca9 100644
--- a/vignettes/solar-examples.Rmd
+++ b/vignettes/solar-examples.Rmd
@@ -6,7 +6,7 @@ output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{GenEst - 2. A Solar Example with Carcass Size}
%\VignetteEngine{knitr::rmarkdown}
- \usepackage[utf8]{inputenc}
+ %\usepackage[utf8]{inputenc}
---
```{r setup, include = FALSE}
diff --git a/vignettes/wind-examples.Rmd b/vignettes/wind-examples.Rmd
index d4324b4fed9bde3c231913f1e954333a563e6a8e..12538fd7eee79f623bb78607274fa70516a36bf7 100644
--- a/vignettes/wind-examples.Rmd
+++ b/vignettes/wind-examples.Rmd
@@ -6,7 +6,7 @@ output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{GenEst - 1. A Tutorial with Wind Examples}
%\VignetteEngine{knitr::rmarkdown}
- \usepackage[utf8]{inputenc}
+ %\usepackage[utf8]{inputenc}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(